Pub Date : 2025-01-07DOI: 10.1007/s11548-024-03310-z
Paul Kaftan, Mattias P Heinrich, Lasse Hansen, Volker Rasche, Hans A Kestler, Alexander Bigalke
Purpose: Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We propose to make lung fissure segmentation more efficient by using geometric deep learning (GDL) on sparse point clouds.
Methods: We abstract image data with sparse keypoint (KP) clouds. We train GDL models to segment the point cloud, comparing three major paradigms of models (PointNets, graph convolutional networks (GCNs), and PointTransformers). From the sparse point segmentations, 3D meshes of the objects are reconstructed to obtain a dense surface. The state-of-the-art Poisson surface reconstruction (PSR) makes up most of the time in our pipeline. Therefore, we propose an efficient point cloud to mesh autoencoder (PC-AE) that deforms a template mesh to fit a point cloud in a single forward pass. Our pipeline is evaluated extensively and compared to the 3D-CNN gold standard nnU-Net on diverse clinical and pathological data.
Results: GCNs yield the best trade-off between inference time and accuracy, being faster with only increased error over the nnU-Net. Our PC-AE also achieves a favorable trade-off, being faster at the error compared to the PSR.
Conclusion: We present a KP-based fissure segmentation pipeline that is more efficient than 3D-CNNs and can greatly speed up large-scale analyses. A novel PC-AE for efficient mesh reconstruction from sparse point clouds is introduced, showing promise not only for fissure segmentation. Source code is available on https://github.com/kaftanski/fissure-segmentation-IJCARS.
{"title":"Sparse keypoint segmentation of lung fissures: efficient geometric deep learning for abstracting volumetric images.","authors":"Paul Kaftan, Mattias P Heinrich, Lasse Hansen, Volker Rasche, Hans A Kestler, Alexander Bigalke","doi":"10.1007/s11548-024-03310-z","DOIUrl":"https://doi.org/10.1007/s11548-024-03310-z","url":null,"abstract":"<p><strong>Purpose: </strong>Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We propose to make lung fissure segmentation more efficient by using geometric deep learning (GDL) on sparse point clouds.</p><p><strong>Methods: </strong>We abstract image data with sparse keypoint (KP) clouds. We train GDL models to segment the point cloud, comparing three major paradigms of models (PointNets, graph convolutional networks (GCNs), and PointTransformers). From the sparse point segmentations, 3D meshes of the objects are reconstructed to obtain a dense surface. The state-of-the-art Poisson surface reconstruction (PSR) makes up most of the time in our pipeline. Therefore, we propose an efficient point cloud to mesh autoencoder (PC-AE) that deforms a template mesh to fit a point cloud in a single forward pass. Our pipeline is evaluated extensively and compared to the 3D-CNN gold standard nnU-Net on diverse clinical and pathological data.</p><p><strong>Results: </strong>GCNs yield the best trade-off between inference time and accuracy, being <math><mrow><mn>21</mn> <mo>×</mo></mrow> </math> faster with only <math><mrow><mn>1.4</mn> <mo>×</mo></mrow> </math> increased error over the nnU-Net. Our PC-AE also achieves a favorable trade-off, being <math><mrow><mn>3</mn> <mo>×</mo></mrow> </math> faster at <math><mrow><mn>1.5</mn> <mo>×</mo></mrow> </math> the error compared to the PSR.</p><p><strong>Conclusion: </strong>We present a KP-based fissure segmentation pipeline that is more efficient than 3D-CNNs and can greatly speed up large-scale analyses. A novel PC-AE for efficient mesh reconstruction from sparse point clouds is introduced, showing promise not only for fissure segmentation. Source code is available on https://github.com/kaftanski/fissure-segmentation-IJCARS.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-05DOI: 10.1007/s11548-024-03302-z
Moujan Saderi, Jaykumar H Patel, Calder D Sheagren, Judit Csőre, Trisha L Roy, Graham A Wright
Purpose: During endovascular revascularization interventions for peripheral arterial disease, the standard modality of X-ray fluoroscopy (XRF) used for image guidance is limited in visualizing distal segments of infrapopliteal vessels. To enhance visualization of arteries, an image registration technique was developed to align pre-acquired computed tomography (CT) angiography images and to create fusion images highlighting arteries of interest.
Methods: X-ray image metadata capturing the position of the X-ray gantry initializes a multiscale iterative optimization process, which uses a local-variance masked normalized cross-correlation loss to rigidly align a digitally reconstructed radiograph (DRR) of the CT dataset with the target X-ray, using the edges of the fibula and tibia as the basis for alignment. A precomputed library of DRRs is used to improve run-time, and the six-degree-of-freedom optimization problem of rigid registration is divided into three smaller sub-problems to improve convergence. The method was tested on a dataset of paired cone-beam CT (CBCT) and XRF images of ex vivo limbs, and registration accuracy at the midline of the artery was evaluated.
Results: On a dataset of CBCTs from 4 different limbs and a total of 17 XRF images, successful registration was achieved in 13 cases, with the remainder suffering from input image quality issues. The method produced average misalignments of less than 1 mm in horizontal projection distance along the artery midline, with an average run-time of 16 s.
Conclusion: The sub-mm spatial accuracy of artery overlays is sufficient for the clinical use case of identifying guidewire deviations from the path of the artery, for early detection of guidewire-induced perforations. The semiautomatic workflow and average run-time of the algorithm make it feasible for integration into clinical workflows.
{"title":"3D CT to 2D X-ray image registration for improved visualization of tibial vessels in endovascular procedures.","authors":"Moujan Saderi, Jaykumar H Patel, Calder D Sheagren, Judit Csőre, Trisha L Roy, Graham A Wright","doi":"10.1007/s11548-024-03302-z","DOIUrl":"https://doi.org/10.1007/s11548-024-03302-z","url":null,"abstract":"<p><strong>Purpose: </strong>During endovascular revascularization interventions for peripheral arterial disease, the standard modality of X-ray fluoroscopy (XRF) used for image guidance is limited in visualizing distal segments of infrapopliteal vessels. To enhance visualization of arteries, an image registration technique was developed to align pre-acquired computed tomography (CT) angiography images and to create fusion images highlighting arteries of interest.</p><p><strong>Methods: </strong>X-ray image metadata capturing the position of the X-ray gantry initializes a multiscale iterative optimization process, which uses a local-variance masked normalized cross-correlation loss to rigidly align a digitally reconstructed radiograph (DRR) of the CT dataset with the target X-ray, using the edges of the fibula and tibia as the basis for alignment. A precomputed library of DRRs is used to improve run-time, and the six-degree-of-freedom optimization problem of rigid registration is divided into three smaller sub-problems to improve convergence. The method was tested on a dataset of paired cone-beam CT (CBCT) and XRF images of ex vivo limbs, and registration accuracy at the midline of the artery was evaluated.</p><p><strong>Results: </strong>On a dataset of CBCTs from 4 different limbs and a total of 17 XRF images, successful registration was achieved in 13 cases, with the remainder suffering from input image quality issues. The method produced average misalignments of less than 1 mm in horizontal projection distance along the artery midline, with an average run-time of 16 s.</p><p><strong>Conclusion: </strong>The sub-mm spatial accuracy of artery overlays is sufficient for the clinical use case of identifying guidewire deviations from the path of the artery, for early detection of guidewire-induced perforations. The semiautomatic workflow and average run-time of the algorithm make it feasible for integration into clinical workflows.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142928132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-04DOI: 10.1007/s11548-024-03311-y
Lovis Schwenderling, Laura Isabel Hanke, Undine Holst, Florentine Huettl, Fabian Joeres, Tobias Huber, Christian Hansen
Purpose: Structured abdominal examination is an essential part of the medical curriculum and surgical training, requiring a blend of theory and practice from trainees. Current training methods, however, often do not provide adequate engagement, fail to address individual learning needs or do not cover rare diseases.
Methods: In this work, an application for structured Abdominal Examination Training using Augmented Reality (AETAR) is presented. Required theoretical knowledge is displayed step by step via virtual indicators directly on the associated body regions. Exercises facilitate building up the routine in performing the examination. AETAR was evaluated in an exploratory user study with medical students (n=12) and teaching surgeons (n=2).
Results: Learning with AETAR was described as fun and beneficial. Usability (SUS=73) and rated suitability for teaching were promising. All students improved in a knowledge test and felt more confident with the abdominal examination. Shortcomings were identified in the area of interaction, especially in teaching examination-specific movements.
Conclusion: AETAR represents a first approach to structured abdominal examination training using augmented reality. The application demonstrates the potential to improve educational outcomes for medical students and provides an important foundation for future research and development in digital medical education.
{"title":"Toward structured abdominal examination training using augmented reality.","authors":"Lovis Schwenderling, Laura Isabel Hanke, Undine Holst, Florentine Huettl, Fabian Joeres, Tobias Huber, Christian Hansen","doi":"10.1007/s11548-024-03311-y","DOIUrl":"https://doi.org/10.1007/s11548-024-03311-y","url":null,"abstract":"<p><strong>Purpose: </strong>Structured abdominal examination is an essential part of the medical curriculum and surgical training, requiring a blend of theory and practice from trainees. Current training methods, however, often do not provide adequate engagement, fail to address individual learning needs or do not cover rare diseases.</p><p><strong>Methods: </strong>In this work, an application for structured Abdominal Examination Training using Augmented Reality (AETAR) is presented. Required theoretical knowledge is displayed step by step via virtual indicators directly on the associated body regions. Exercises facilitate building up the routine in performing the examination. AETAR was evaluated in an exploratory user study with medical students (n=12) and teaching surgeons (n=2).</p><p><strong>Results: </strong>Learning with AETAR was described as fun and beneficial. Usability (SUS=73) and rated suitability for teaching were promising. All students improved in a knowledge test and felt more confident with the abdominal examination. Shortcomings were identified in the area of interaction, especially in teaching examination-specific movements.</p><p><strong>Conclusion: </strong>AETAR represents a first approach to structured abdominal examination training using augmented reality. The application demonstrates the potential to improve educational outcomes for medical students and provides an important foundation for future research and development in digital medical education.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142928249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-09-20DOI: 10.1007/s11548-024-03275-z
Yuuki Shida, Souto Kumagai, Hiroyasu Iwata
Purpose: The search for heart components in robotic transthoracic echocardiography is a time-consuming process. This paper proposes an optimized robotic navigation system for heart components using deep reinforcement learning to achieve an efficient and effective search technique for heart components.
Method: The proposed method introduces (i) an optimized search behavior generation algorithm that avoids multiple local solutions and searches for the optimal solution and (ii) an optimized path generation algorithm that minimizes the search path, thereby realizing short search times.
Results: The mitral valve search with the proposed method reaches the optimal solution with a probability of 74.4%, the mitral valve confidence loss rate when the local solution stops is 16.3% on average, and the inspection time with the generated path is 48.6 s on average, which is 56.6% of the time cost of the conventional method.
Conclusion: The results indicate that the proposed method improves the search efficiency, and the optimal location can be searched in many cases with the proposed method, and the loss rate of the confidence in the mitral valve was low even when a local solution rather than the optimal solution was reached. It is suggested that the proposed method enables accurate and quick robotic navigation to find heart components.
{"title":"Robotic navigation with deep reinforcement learning in transthoracic echocardiography.","authors":"Yuuki Shida, Souto Kumagai, Hiroyasu Iwata","doi":"10.1007/s11548-024-03275-z","DOIUrl":"10.1007/s11548-024-03275-z","url":null,"abstract":"<p><strong>Purpose: </strong>The search for heart components in robotic transthoracic echocardiography is a time-consuming process. This paper proposes an optimized robotic navigation system for heart components using deep reinforcement learning to achieve an efficient and effective search technique for heart components.</p><p><strong>Method: </strong>The proposed method introduces (i) an optimized search behavior generation algorithm that avoids multiple local solutions and searches for the optimal solution and (ii) an optimized path generation algorithm that minimizes the search path, thereby realizing short search times.</p><p><strong>Results: </strong>The mitral valve search with the proposed method reaches the optimal solution with a probability of 74.4%, the mitral valve confidence loss rate when the local solution stops is 16.3% on average, and the inspection time with the generated path is 48.6 s on average, which is 56.6% of the time cost of the conventional method.</p><p><strong>Conclusion: </strong>The results indicate that the proposed method improves the search efficiency, and the optimal location can be searched in many cases with the proposed method, and the loss rate of the confidence in the mitral valve was low even when a local solution rather than the optimal solution was reached. It is suggested that the proposed method enables accurate and quick robotic navigation to find heart components.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"191-202"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11757869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-06-07DOI: 10.1007/s11548-024-03131-0
Hamraz Javaheri, Omid Ghamarnejad, Ragnar Bade, Paul Lukowicz, Jakob Karolus, Gregor Alexander Stavrou
Purpose: The retroperitoneal nature of the pancreas, marked by minimal intraoperative organ shifts and deformations, makes augmented reality (AR)-based systems highly promising for pancreatic surgery. This study presents preliminary data from a prospective study aiming to develop the first wearable AR assistance system, ARAS, for pancreatic surgery and evaluating its usability, accuracy, and effectiveness in enhancing the perioperative outcomes of patients.
Methods: We developed ARAS as a two-phase system for a wearable AR device to aid surgeons in planning and operation. This system was used to visualize and register patient-specific 3D anatomical models during the surgery. The location and precision of the registered 3D anatomy were evaluated by assessing the arterial pulse and employing Doppler and duplex ultrasonography. The usability, accuracy, and effectiveness of ARAS were assessed using a five-point Likert scale questionnaire.
Results: Perioperative outcomes of five patients underwent various pancreatic resections with ARAS are presented. Surgeons rated ARAS as excellent for preoperative planning. All structures were accurately identified without any noteworthy errors. Only tumor identification decreased after the preparation phase, especially in patients who underwent pancreaticoduodenectomy because of the extensive mobilization of peripancreatic structures. No perioperative complications related to ARAS were observed.
Conclusions: ARAS shows promise in enhancing surgical precision during pancreatic procedures. Its efficacy in preoperative planning and intraoperative vascular identification positions it as a valuable tool for pancreatic surgery and a potential educational resource for future surgical residents.
目的:胰腺位于腹膜后,术中器官移位和变形极小,这使得基于增强现实(AR)的系统在胰腺手术中大有可为。本研究介绍了一项前瞻性研究的初步数据,该研究旨在开发首个用于胰腺手术的可穿戴 AR 辅助系统 ARAS,并评估其可用性、准确性以及在提高患者围手术期效果方面的有效性:我们开发的ARAS是一个可穿戴AR设备的两阶段系统,用于辅助外科医生制定计划和进行手术。该系统用于在手术过程中可视化和注册患者特定的三维解剖模型。通过评估动脉脉搏以及使用多普勒和双相超声波检查,对注册的三维解剖模型的位置和精确度进行了评估。使用李克特五点量表问卷对 ARAS 的可用性、准确性和有效性进行了评估:结果:本文介绍了五名使用 ARAS 进行各种胰腺切除术的患者的围手术期结果。外科医生认为ARAS在术前规划方面表现出色。所有结构都能准确识别,没有任何值得注意的错误。只有肿瘤识别率在准备阶段后有所下降,特别是在接受胰十二指肠切除术的患者中,因为需要广泛移动胰腺周围结构。没有观察到与ARAS相关的围手术期并发症:结论:ARAS有望提高胰腺手术的精确度。ARAS在术前规划和术中血管识别方面的功效使其成为胰腺手术的重要工具,也是未来外科住院医生的潜在教育资源。
{"title":"Beyond the visible: preliminary evaluation of the first wearable augmented reality assistance system for pancreatic surgery.","authors":"Hamraz Javaheri, Omid Ghamarnejad, Ragnar Bade, Paul Lukowicz, Jakob Karolus, Gregor Alexander Stavrou","doi":"10.1007/s11548-024-03131-0","DOIUrl":"10.1007/s11548-024-03131-0","url":null,"abstract":"<p><strong>Purpose: </strong>The retroperitoneal nature of the pancreas, marked by minimal intraoperative organ shifts and deformations, makes augmented reality (AR)-based systems highly promising for pancreatic surgery. This study presents preliminary data from a prospective study aiming to develop the first wearable AR assistance system, ARAS, for pancreatic surgery and evaluating its usability, accuracy, and effectiveness in enhancing the perioperative outcomes of patients.</p><p><strong>Methods: </strong>We developed ARAS as a two-phase system for a wearable AR device to aid surgeons in planning and operation. This system was used to visualize and register patient-specific 3D anatomical models during the surgery. The location and precision of the registered 3D anatomy were evaluated by assessing the arterial pulse and employing Doppler and duplex ultrasonography. The usability, accuracy, and effectiveness of ARAS were assessed using a five-point Likert scale questionnaire.</p><p><strong>Results: </strong>Perioperative outcomes of five patients underwent various pancreatic resections with ARAS are presented. Surgeons rated ARAS as excellent for preoperative planning. All structures were accurately identified without any noteworthy errors. Only tumor identification decreased after the preparation phase, especially in patients who underwent pancreaticoduodenectomy because of the extensive mobilization of peripancreatic structures. No perioperative complications related to ARAS were observed.</p><p><strong>Conclusions: </strong>ARAS shows promise in enhancing surgical precision during pancreatic procedures. Its efficacy in preoperative planning and intraoperative vascular identification positions it as a valuable tool for pancreatic surgery and a potential educational resource for future surgical residents.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"117-129"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11757645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141288907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-09-06DOI: 10.1007/s11548-024-03255-3
William Ndzimbong, Nicolas Thome, Cyril Fourniol, Yvonne Keeza, Benoît Sauer, Jacques Marescaux, Daniel George, Alexandre Hostettler, Toby Collins
Purpose: Automatic registration between abdominal ultrasound (US) and computed tomography (CT) images is needed to enhance interventional guidance of renal procedures, but it remains an open research challenge. We propose a novel method that doesn't require an initial registration estimate (a global method) and also handles registration ambiguity caused by the organ's natural symmetry. Combined with a registration refinement algorithm, this method achieves robust and accurate kidney registration while avoiding manual initialization.
Methods: We propose solving global registration in a three-step approach: (1) Automatic anatomical landmark localization, where 2 deep neural networks (DNNs) localize a set of landmarks in each modality. (2) Registration hypothesis generation, where potential registrations are computed from the landmarks with a deterministic variant of RANSAC. Due to the Kidney's strong bilateral symmetry, there are usually 2 compatible solutions. Finally, in Step (3), the correct solution is determined automatically, using a DNN classifier that resolves the geometric ambiguity. The registration may then be iteratively improved with a registration refinement method. Results are presented with state-of-the-art surface-based refinement-Bayesian coherent point drift (BCPD).
Results: This automatic global registration approach gives better results than various competitive state-of-the-art methods, which, additionally, require organ segmentation. The results obtained on 59 pairs of 3D US/CT kidney images show that the proposed method, combined with BCPD refinement, achieves a target registration error (TRE) of an internal kidney landmark (the renal pelvis) of 5.78 mm and an average nearest neighbor surface distance (nndist) of 2.42 mm.
Conclusion: This work presents the first approach for automatic kidney registration in US and CT images, which doesn't require an initial manual registration estimate to be known a priori. The results show a fully automatic registration approach with performances comparable to manual methods is feasible.
{"title":"Global registration of kidneys in 3D ultrasound and CT images.","authors":"William Ndzimbong, Nicolas Thome, Cyril Fourniol, Yvonne Keeza, Benoît Sauer, Jacques Marescaux, Daniel George, Alexandre Hostettler, Toby Collins","doi":"10.1007/s11548-024-03255-3","DOIUrl":"10.1007/s11548-024-03255-3","url":null,"abstract":"<p><strong>Purpose: </strong>Automatic registration between abdominal ultrasound (US) and computed tomography (CT) images is needed to enhance interventional guidance of renal procedures, but it remains an open research challenge. We propose a novel method that doesn't require an initial registration estimate (a global method) and also handles registration ambiguity caused by the organ's natural symmetry. Combined with a registration refinement algorithm, this method achieves robust and accurate kidney registration while avoiding manual initialization.</p><p><strong>Methods: </strong>We propose solving global registration in a three-step approach: (1) Automatic anatomical landmark localization, where 2 deep neural networks (DNNs) localize a set of landmarks in each modality. (2) Registration hypothesis generation, where potential registrations are computed from the landmarks with a deterministic variant of RANSAC. Due to the Kidney's strong bilateral symmetry, there are usually 2 compatible solutions. Finally, in Step (3), the correct solution is determined automatically, using a DNN classifier that resolves the geometric ambiguity. The registration may then be iteratively improved with a registration refinement method. Results are presented with state-of-the-art surface-based refinement-Bayesian coherent point drift (BCPD).</p><p><strong>Results: </strong>This automatic global registration approach gives better results than various competitive state-of-the-art methods, which, additionally, require organ segmentation. The results obtained on 59 pairs of 3D US/CT kidney images show that the proposed method, combined with BCPD refinement, achieves a target registration error (TRE) of an internal kidney landmark (the renal pelvis) of 5.78 mm and an average nearest neighbor surface distance (nndist) of 2.42 mm.</p><p><strong>Conclusion: </strong>This work presents the first approach for automatic kidney registration in US and CT images, which doesn't require an initial manual registration estimate to be known a priori. The results show a fully automatic registration approach with performances comparable to manual methods is feasible.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"65-75"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142146830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-05-13DOI: 10.1007/s11548-024-03153-8
Ainkaran Santhirasekaram, Mathias Winkler, Andrea Rockall, Ben Glocker
Purpose: Automated prostate disease classification on multi-parametric MRI has recently shown promising results with the use of convolutional neural networks (CNNs). The vision transformer (ViT) is a convolutional free architecture which only exploits the self-attention mechanism and has surpassed CNNs in some natural imaging classification tasks. However, these models are not very robust to textural shifts in the input space. In MRI, we often have to deal with textural shift arising from varying acquisition protocols. Here, we focus on the ability of models to generalise well to new magnet strengths for MRI.
Method: We propose a new framework to improve the robustness of vision transformer-based models for disease classification by constructing discrete representations of the data using vector quantisation. We sample a subset of the discrete representations to form the input into a transformer-based model. We use cross-attention in our transformer model to combine the discrete representations of T2-weighted and apparent diffusion coefficient (ADC) images.
Results: We analyse the robustness of our model by training on a 1.5 T scanner and test on a 3 T scanner and vice versa. Our approach achieves SOTA performance for classification of lesions on prostate MRI and outperforms various other CNN and transformer-based models in terms of robustness to domain shift and perturbations in the input space.
Conclusion: We develop a method to improve the robustness of transformer-based disease classification of prostate lesions on MRI using discrete representations of the T2-weighted and ADC images.
目的:最近,使用卷积神经网络(CNN)对多参数磁共振成像进行前列腺疾病自动分类取得了可喜的成果。视觉转换器(ViT)是一种卷积自由架构,它只利用了自注意机制,在一些自然成像分类任务中已经超越了 CNN。然而,这些模型对输入空间的纹理变化并不十分稳健。在核磁共振成像中,我们经常需要处理因不同采集协议而产生的纹理偏移。在此,我们将重点关注模型对 MRI 新磁铁强度的良好泛化能力:方法:我们提出了一个新框架,通过使用向量量化来构建数据的离散表示,从而提高基于视觉变换器的疾病分类模型的鲁棒性。我们对离散表示的一个子集进行采样,以形成基于转换器的模型的输入。我们在变压器模型中使用交叉注意,将 T2 加权图像和表观扩散系数(ADC)图像的离散表示结合起来:我们通过在 1.5 T 扫描仪上进行训练和在 3 T 扫描仪上进行测试来分析模型的鲁棒性,反之亦然。我们的方法在前列腺磁共振成像病变分类方面实现了 SOTA 性能,在对输入空间的域偏移和扰动的鲁棒性方面优于其他各种基于 CNN 和变压器的模型:我们开发了一种方法,利用 T2 加权和 ADC 图像的离散表示,提高了基于变压器的前列腺 MRI 病变分类的鲁棒性。
{"title":"Robust prostate disease classification using transformers with discrete representations.","authors":"Ainkaran Santhirasekaram, Mathias Winkler, Andrea Rockall, Ben Glocker","doi":"10.1007/s11548-024-03153-8","DOIUrl":"10.1007/s11548-024-03153-8","url":null,"abstract":"<p><strong>Purpose: </strong>Automated prostate disease classification on multi-parametric MRI has recently shown promising results with the use of convolutional neural networks (CNNs). The vision transformer (ViT) is a convolutional free architecture which only exploits the self-attention mechanism and has surpassed CNNs in some natural imaging classification tasks. However, these models are not very robust to textural shifts in the input space. In MRI, we often have to deal with textural shift arising from varying acquisition protocols. Here, we focus on the ability of models to generalise well to new magnet strengths for MRI.</p><p><strong>Method: </strong>We propose a new framework to improve the robustness of vision transformer-based models for disease classification by constructing discrete representations of the data using vector quantisation. We sample a subset of the discrete representations to form the input into a transformer-based model. We use cross-attention in our transformer model to combine the discrete representations of T2-weighted and apparent diffusion coefficient (ADC) images.</p><p><strong>Results: </strong>We analyse the robustness of our model by training on a 1.5 T scanner and test on a 3 T scanner and vice versa. Our approach achieves SOTA performance for classification of lesions on prostate MRI and outperforms various other CNN and transformer-based models in terms of robustness to domain shift and perturbations in the input space.</p><p><strong>Conclusion: </strong>We develop a method to improve the robustness of transformer-based disease classification of prostate lesions on MRI using discrete representations of the T2-weighted and ADC images.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"11-20"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140916593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-09-09DOI: 10.1007/s11548-024-03260-6
Sven Kolb, Andrew Madden, Nicolai Kröger, Fidan Mehmeti, Franziska Jurosch, Lukas Bernhard, Wolfgang Kellerer, Dirk Wilhelm
Purpose: Healthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network standards.
Methods: We developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs).
Results: The results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical robotics.
Conclusion: This work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.
{"title":"6G in medical robotics: development of network allocation strategies for a telerobotic examination system.","authors":"Sven Kolb, Andrew Madden, Nicolai Kröger, Fidan Mehmeti, Franziska Jurosch, Lukas Bernhard, Wolfgang Kellerer, Dirk Wilhelm","doi":"10.1007/s11548-024-03260-6","DOIUrl":"10.1007/s11548-024-03260-6","url":null,"abstract":"<p><strong>Purpose: </strong>Healthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network standards.</p><p><strong>Methods: </strong>We developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs).</p><p><strong>Results: </strong>The results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical robotics.</p><p><strong>Conclusion: </strong>This work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"167-178"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-05-20DOI: 10.1007/s11548-024-03185-0
George R Nahass, Mitchell A Marques, Naji Bou Zeid, Linping Zhao, Lee W T Alkureishi
Purpose: Age-matched average 3D models facilitate both surgical planning and intraoperative guidance of cranial birth defects such as craniosynostosis. We aimed to develop an algorithm that accepts any number of CT scans as input and generates highly accurate, average models with minimal user input that are ready for 3D printing and clinical use.
Methods: Using a compiled database of 'normal' pediatric computed tomography (CT) scans, we report Normscan, an open-source platform built in Python that allows users to generate normative models of CT scans through user-defined landmarks. We use the basion, nasion, and left and right porions as anatomical landmarks for initial correspondence and then register the models using the iterative closest points algorithm before downstream averaging.
Results: Normscan is fast and easy to use via our user interface and also creates highly accurate average models of any number of input models. Additionally, it is highly repeatable, with coefficients of variance for the surface area and volume of the average model being less than 3% across ten independent trials. Average models can then be 3D printed and/or visualized in augmented reality.
Conclusions: Normscan provides an end-to-end pipeline for the creation of average models of skulls. These models can be used for the generation of databases of specific demographic anatomical models as well as for intraoperative guidance and surgical planning. While Normscan was designed for craniosynostosis repair, due to the modular nature of the algorithm, Normscan has many applications in other areas of surgical planning and research.
{"title":"Normscan: open-source Python software to create average models from CT scans.","authors":"George R Nahass, Mitchell A Marques, Naji Bou Zeid, Linping Zhao, Lee W T Alkureishi","doi":"10.1007/s11548-024-03185-0","DOIUrl":"10.1007/s11548-024-03185-0","url":null,"abstract":"<p><strong>Purpose: </strong>Age-matched average 3D models facilitate both surgical planning and intraoperative guidance of cranial birth defects such as craniosynostosis. We aimed to develop an algorithm that accepts any number of CT scans as input and generates highly accurate, average models with minimal user input that are ready for 3D printing and clinical use.</p><p><strong>Methods: </strong>Using a compiled database of 'normal' pediatric computed tomography (CT) scans, we report Normscan, an open-source platform built in Python that allows users to generate normative models of CT scans through user-defined landmarks. We use the basion, nasion, and left and right porions as anatomical landmarks for initial correspondence and then register the models using the iterative closest points algorithm before downstream averaging.</p><p><strong>Results: </strong>Normscan is fast and easy to use via our user interface and also creates highly accurate average models of any number of input models. Additionally, it is highly repeatable, with coefficients of variance for the surface area and volume of the average model being less than 3% across ten independent trials. Average models can then be 3D printed and/or visualized in augmented reality.</p><p><strong>Conclusions: </strong>Normscan provides an end-to-end pipeline for the creation of average models of skulls. These models can be used for the generation of databases of specific demographic anatomical models as well as for intraoperative guidance and surgical planning. While Normscan was designed for craniosynostosis repair, due to the modular nature of the algorithm, Normscan has many applications in other areas of surgical planning and research.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"157-165"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141066455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-07-31DOI: 10.1007/s11548-024-03240-w
Elia Halle, Tevel Amiel, Doron J Aframian, Tal Malik, Avital Rozenthal, Oren Shauly, Leo Joskowicz, Chen Nadler, Talia Yeshua
Purpose: This study addressed the challenge of detecting and classifying the severity of ductopenia in parotid glands, a structural abnormality characterized by a reduced number of salivary ducts, previously shown to be associated with salivary gland impairment. The aim of the study was to develop an automatic algorithm designed to improve diagnostic accuracy and efficiency in analyzing ductopenic parotid glands using sialo cone-beam CT (sialo-CBCT) images.
Methods: We developed an end-to-end automatic pipeline consisting of three main steps: (1) region of interest (ROI) computation, (2) parotid gland segmentation using the Frangi filter, and (3) ductopenia case classification with a residual neural network (RNN) augmented by multidirectional maximum intensity projection (MIP) images. To explore the impact of the first two steps, the RNN was trained on three datasets: (1) original MIP images, (2) MIP images with predefined ROIs, and (3) MIP images after segmentation.
Results: Evaluation was conducted on 126 parotid sialo-CBCT scans of normal, moderate, and severe ductopenic cases, yielding a high performance of 100% for the ROI computation and 89% for the gland segmentation. Improvements in accuracy and F1 score were noted among the original MIP images (accuracy: 0.73, F1 score: 0.53), ROI-predefined images (accuracy: 0.78, F1 score: 0.56), and segmented images (accuracy: 0.95, F1 score: 0.90). Notably, ductopenic detection sensitivity was 0.99 in the segmented dataset, highlighting the capabilities of the algorithm in detecting ductopenic cases.
Conclusions: Our method, which combines classical image processing and deep learning techniques, offers a promising solution for automatic detection of parotid glands ductopenia in sialo-CBCT scans. This may be used for further research aimed at understanding the role of presence and severity of ductopenia in salivary gland dysfunction.
{"title":"Automated segmentation and deep learning classification of ductopenic parotid salivary glands in sialo cone-beam CT images.","authors":"Elia Halle, Tevel Amiel, Doron J Aframian, Tal Malik, Avital Rozenthal, Oren Shauly, Leo Joskowicz, Chen Nadler, Talia Yeshua","doi":"10.1007/s11548-024-03240-w","DOIUrl":"10.1007/s11548-024-03240-w","url":null,"abstract":"<p><strong>Purpose: </strong>This study addressed the challenge of detecting and classifying the severity of ductopenia in parotid glands, a structural abnormality characterized by a reduced number of salivary ducts, previously shown to be associated with salivary gland impairment. The aim of the study was to develop an automatic algorithm designed to improve diagnostic accuracy and efficiency in analyzing ductopenic parotid glands using sialo cone-beam CT (sialo-CBCT) images.</p><p><strong>Methods: </strong>We developed an end-to-end automatic pipeline consisting of three main steps: (1) region of interest (ROI) computation, (2) parotid gland segmentation using the Frangi filter, and (3) ductopenia case classification with a residual neural network (RNN) augmented by multidirectional maximum intensity projection (MIP) images. To explore the impact of the first two steps, the RNN was trained on three datasets: (1) original MIP images, (2) MIP images with predefined ROIs, and (3) MIP images after segmentation.</p><p><strong>Results: </strong>Evaluation was conducted on 126 parotid sialo-CBCT scans of normal, moderate, and severe ductopenic cases, yielding a high performance of 100% for the ROI computation and 89% for the gland segmentation. Improvements in accuracy and F1 score were noted among the original MIP images (accuracy: 0.73, F1 score: 0.53), ROI-predefined images (accuracy: 0.78, F1 score: 0.56), and segmented images (accuracy: 0.95, F1 score: 0.90). Notably, ductopenic detection sensitivity was 0.99 in the segmented dataset, highlighting the capabilities of the algorithm in detecting ductopenic cases.</p><p><strong>Conclusions: </strong>Our method, which combines classical image processing and deep learning techniques, offers a promising solution for automatic detection of parotid glands ductopenia in sialo-CBCT scans. This may be used for further research aimed at understanding the role of presence and severity of ductopenia in salivary gland dysfunction.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"21-30"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}