Pub Date : 2021-04-13DOI: 10.1109/ISBI48211.2021.9433804
Wanwen Chen, Kathan Nilesh Mehta, Bhumi Dinesh Bhanushali, J. Galeotti
We present a novel algorithm for needle tracking in ultrasound-guided needle insertion. Most previous research assumes that in ultrasound images the needle is a straight and bright line, but needles can bend due to the interaction with heterogeneous tissue. We utilize a novel weighted RANSAC curve fitting method combined with probabilistic Hough transform to track the curved needle robustly, and the algorithm can additionally utilize external tracking information, such as robotic kinematics, to further improve the tracking accuracy. We compared against classical tracking algorithms and a U-Net model, testing over different needle curvature and tissues. Our proposed algorithm achieves higher accuracy in tip location, shaft fitting, and tip angle. In-vivo porcine experiments with naturally bending short needles also show our method better tracked the tip location.
{"title":"Ultrasound-Based Tracking Of Partially In-Plane, Curved Needles","authors":"Wanwen Chen, Kathan Nilesh Mehta, Bhumi Dinesh Bhanushali, J. Galeotti","doi":"10.1109/ISBI48211.2021.9433804","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9433804","url":null,"abstract":"We present a novel algorithm for needle tracking in ultrasound-guided needle insertion. Most previous research assumes that in ultrasound images the needle is a straight and bright line, but needles can bend due to the interaction with heterogeneous tissue. We utilize a novel weighted RANSAC curve fitting method combined with probabilistic Hough transform to track the curved needle robustly, and the algorithm can additionally utilize external tracking information, such as robotic kinematics, to further improve the tracking accuracy. We compared against classical tracking algorithms and a U-Net model, testing over different needle curvature and tissues. Our proposed algorithm achieves higher accuracy in tip location, shaft fitting, and tip angle. In-vivo porcine experiments with naturally bending short needles also show our method better tracked the tip location.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129152087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-13DOI: 10.1109/ISBI48211.2021.9434067
F. Danckaers, Jeroen Van Houtte, Brian G. Booth, F. Verstreken, Jan Sijbers
Custom splint design is becoming more common. However, poor 3D scan quality can negatively impact the design accuracy. This paper describes a method to build a 3D statistical shape and pose model of the forearm from 3dMD scans. The model is used to assist the registration of previously unseen forearms in a wide range of poses. We show that this model-based surface registration results in a good geometric fit, with accurate anatomical correspondences. This method could be used to upgrade low-resolution scans using a high-resolution model.
{"title":"Statistical Shape and Pose Model of the Forearm for Custom Splint Design","authors":"F. Danckaers, Jeroen Van Houtte, Brian G. Booth, F. Verstreken, Jan Sijbers","doi":"10.1109/ISBI48211.2021.9434067","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9434067","url":null,"abstract":"Custom splint design is becoming more common. However, poor 3D scan quality can negatively impact the design accuracy. This paper describes a method to build a 3D statistical shape and pose model of the forearm from 3dMD scans. The model is used to assist the registration of previously unseen forearms in a wide range of poses. We show that this model-based surface registration results in a good geometric fit, with accurate anatomical correspondences. This method could be used to upgrade low-resolution scans using a high-resolution model.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"329 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133678508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-13DOI: 10.1109/ISBI48211.2021.9434090
Giammarco La Barbera, P. Gori, Haithem Boussaid, Bruno Belucci, A. Delmonte, Jeanne Goulin, S. Sarnacki, L. Rouet, I. Bloch
Due to a high heterogeneity in pose and size and to a limited number of available data, segmentation of pediatric images is challenging for deep learning methods. In this work, we propose a new CNN architecture that is pose and scale invariant thanks to the use of Spatial Transformer Network (STN). Our architecture is composed of three sequential modules that are estimated together during training: (i) a regression module to estimate a similarity matrix to normalize the input image to a reference one; (ii) a differentiable module to find the region of interest to segment; (iii) a segmentation module, based on the popular UNet architecture, to delineate the object. Unlike the original UNet, which strives to learn a complex mapping, including pose and scale variations, from a finite training dataset, our segmentation module learns a simpler mapping focusing on images with normalized pose and size. Furthermore, the use of an automatic bounding box detection through STN allows saving time and especially memory, while keeping similar performance. We test the proposed method in kidney and renal tumor segmentation on abdominal pediatric CT scanners. Results indicate that the estimated STN homogenization of size and pose accelerates the segmentation (25h), compared to standard data-augmentation (33h), while obtaining a similar quality for the kidney (88.01% of Dice score) and improving the renal tumor delineation (from 85.52% to 87.12%).
{"title":"Automatic Size And Pose Homogenization With Spatial Transformer Network To Improve And Accelerate Pediatric Segmentation","authors":"Giammarco La Barbera, P. Gori, Haithem Boussaid, Bruno Belucci, A. Delmonte, Jeanne Goulin, S. Sarnacki, L. Rouet, I. Bloch","doi":"10.1109/ISBI48211.2021.9434090","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9434090","url":null,"abstract":"Due to a high heterogeneity in pose and size and to a limited number of available data, segmentation of pediatric images is challenging for deep learning methods. In this work, we propose a new CNN architecture that is pose and scale invariant thanks to the use of Spatial Transformer Network (STN). Our architecture is composed of three sequential modules that are estimated together during training: (i) a regression module to estimate a similarity matrix to normalize the input image to a reference one; (ii) a differentiable module to find the region of interest to segment; (iii) a segmentation module, based on the popular UNet architecture, to delineate the object. Unlike the original UNet, which strives to learn a complex mapping, including pose and scale variations, from a finite training dataset, our segmentation module learns a simpler mapping focusing on images with normalized pose and size. Furthermore, the use of an automatic bounding box detection through STN allows saving time and especially memory, while keeping similar performance. We test the proposed method in kidney and renal tumor segmentation on abdominal pediatric CT scanners. Results indicate that the estimated STN homogenization of size and pose accelerates the segmentation (25h), compared to standard data-augmentation (33h), while obtaining a similar quality for the kidney (88.01% of Dice score) and improving the renal tumor delineation (from 85.52% to 87.12%).","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130349892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-13DOI: 10.1109/ISBI48211.2021.9433916
Yu Luo, Haoyang Chen, Jicong Zhang
The intensive practice of specific cognitive activities can lead to improvements of relevant cognitive capability in human beings, which may transfer to gain in untrained activities. Although there are a growing number of studies investigating the behavioral benefits of attention training in mind wandering, few studies have directly examined the neurophysiological basis of the training effects. Here using 128-channel electroencephalography (EEG), we examined whether the tactile training can reduce the mind wandering as measured by the sustained attention to response task (SART), and how the dynamic neurophysiological connectivity changes following training in young adults. The trainees showed significantly less occurrence of mind wandering after the five-day tactile training. Furthermore, the functional connectivity within and between the frontal and parietal regions was enhanced after training. Our findings suggest that the tactile training-induced brain plasticity may provide new therapeutic strategies for attention-related disorders.
{"title":"Enhanced Connectivity and Reduced Mind Wandering after Tactile Training in Young Adults","authors":"Yu Luo, Haoyang Chen, Jicong Zhang","doi":"10.1109/ISBI48211.2021.9433916","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9433916","url":null,"abstract":"The intensive practice of specific cognitive activities can lead to improvements of relevant cognitive capability in human beings, which may transfer to gain in untrained activities. Although there are a growing number of studies investigating the behavioral benefits of attention training in mind wandering, few studies have directly examined the neurophysiological basis of the training effects. Here using 128-channel electroencephalography (EEG), we examined whether the tactile training can reduce the mind wandering as measured by the sustained attention to response task (SART), and how the dynamic neurophysiological connectivity changes following training in young adults. The trainees showed significantly less occurrence of mind wandering after the five-day tactile training. Furthermore, the functional connectivity within and between the frontal and parietal regions was enhanced after training. Our findings suggest that the tactile training-induced brain plasticity may provide new therapeutic strategies for attention-related disorders.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130460331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-13DOI: 10.1109/ISBI48211.2021.9433998
Thanh-an Michel Pham, Emmanuel Soubies, F. Soulez, M. Unser
Single-molecule localization microscopy (SMLM) is a fluorescence microscopy technique that achieves super-resolution imaging by sequentially activating and localizing random sparse subsets of fluorophores. Each activated fluorophore emits light that then scatters through the sample, thus acting as a source of illumination from inside the sample. Hence, the sequence of SMLM frames carries information on the distribution of the refractive index of the sample. In this proof-of-concept work, we explore the possibility of exploiting this information to recover the refractive index of the imaged sample, given the localized molecules. Our results with simulated data suggest that it is possible to exploit the phase information that underlies the SMLM data.
{"title":"Diffraction Tomography From Single-Molecule Localization Microscopy: Numerical Feasibility","authors":"Thanh-an Michel Pham, Emmanuel Soubies, F. Soulez, M. Unser","doi":"10.1109/ISBI48211.2021.9433998","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9433998","url":null,"abstract":"Single-molecule localization microscopy (SMLM) is a fluorescence microscopy technique that achieves super-resolution imaging by sequentially activating and localizing random sparse subsets of fluorophores. Each activated fluorophore emits light that then scatters through the sample, thus acting as a source of illumination from inside the sample. Hence, the sequence of SMLM frames carries information on the distribution of the refractive index of the sample. In this proof-of-concept work, we explore the possibility of exploiting this information to recover the refractive index of the imaged sample, given the localized molecules. Our results with simulated data suggest that it is possible to exploit the phase information that underlies the SMLM data.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116894487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-13DOI: 10.1109/ISBI48211.2021.9434145
Priyanka S. Rana, E. Meijering, A. Sowmya, Yang Song
In this paper, we present a multi-label classification pipeline and a novel feature descriptor for the protein subcellular localisation. The challenge here is the development of a computational model that can classify multi-site proteins on a highly imbalanced dataset with a long-tail distribution and multi-label images. To address this challenge, we design a Location-Sorted Random Projections feature descriptor to represent image intensity and gradient of the protein of interest in reference to the correlated cellular region. Multilabel Synthetic Minority Over-sampling Technique is optimised to generate synthetic features with labels to handle class imbalance. Our method achieves the state-of-the-art performance on a large-scale public dataset and demonstrates excellent performance for the minority classes.
{"title":"Multi-Label Classification Based On Subcellular Region-Guided Feature Description For Protein Localisation","authors":"Priyanka S. Rana, E. Meijering, A. Sowmya, Yang Song","doi":"10.1109/ISBI48211.2021.9434145","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9434145","url":null,"abstract":"In this paper, we present a multi-label classification pipeline and a novel feature descriptor for the protein subcellular localisation. The challenge here is the development of a computational model that can classify multi-site proteins on a highly imbalanced dataset with a long-tail distribution and multi-label images. To address this challenge, we design a Location-Sorted Random Projections feature descriptor to represent image intensity and gradient of the protein of interest in reference to the correlated cellular region. Multilabel Synthetic Minority Over-sampling Technique is optimised to generate synthetic features with labels to handle class imbalance. Our method achieves the state-of-the-art performance on a large-scale public dataset and demonstrates excellent performance for the minority classes.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131273566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intrahepatic vascular separation on contrast-enhanced Magnetic Resonance (MR) images is indispensable for the hepatic tumor surgery. This paper presents an unsupervised frame-work based on structural saliency for automatically separating portal vein (PV) and hepatic vein (HV) from contrast-enhanced multi-phase MR images. In our work, we propose a new multi-scale filter based on statistics and shape information in the region of interest, called SSIROI, with which the vascular connectivity and saliency in the 3D hepatic region can be guaranteed. Experiments are conducted on clinical contrast-enhanced MR images, and the results show that our method achieves effective separation of intrahepatic vasculature by extracting the PV and HV from multi-phase images, and our proposed SSIROI filter outperforms state-of-the-art methods.
{"title":"A Structural Saliency-Based Approach for Automatic Intrahepatic Vascular Separation From Contrast-Enhanced Multi-Phase MR Images","authors":"Q. Guo, Hong Song, Jingfan Fan, Danni Ai, Jian Yang, Yuanjin Gao","doi":"10.1109/ISBI48211.2021.9433995","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9433995","url":null,"abstract":"Intrahepatic vascular separation on contrast-enhanced Magnetic Resonance (MR) images is indispensable for the hepatic tumor surgery. This paper presents an unsupervised frame-work based on structural saliency for automatically separating portal vein (PV) and hepatic vein (HV) from contrast-enhanced multi-phase MR images. In our work, we propose a new multi-scale filter based on statistics and shape information in the region of interest, called SSIROI, with which the vascular connectivity and saliency in the 3D hepatic region can be guaranteed. Experiments are conducted on clinical contrast-enhanced MR images, and the results show that our method achieves effective separation of intrahepatic vasculature by extracting the PV and HV from multi-phase images, and our proposed SSIROI filter outperforms state-of-the-art methods.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128451760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-13DOI: 10.1109/ISBI48211.2021.9434080
Angela P. Cuadros, Carlos M. Restrepo, P. Noel
This paper introduces a single-scan dual-energy coded aperture computed tomography system that enables material characterization at a reduced exposure level. Rapid kVp switching with a single-static block/unblock coded aperture relies on coded illumination with a plurality of X-ray spectra created by the kVp switching. Based on the tensor representation of the projection data, an algorithm to estimate the missing measurements in the tensor is proposed. This results in a full set of synthesized measurements that can be used with filtered back-projection or iterative reconstruction algorithms to accurately reconstruct the object in each energy channel. Simulation results validate the effectiveness of the proposed cost-effective solution to attain material characterization in low-dose dual-energy CT.
{"title":"Low-Dose Dual KVP Switching Using A Static Coded Aperture","authors":"Angela P. Cuadros, Carlos M. Restrepo, P. Noel","doi":"10.1109/ISBI48211.2021.9434080","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9434080","url":null,"abstract":"This paper introduces a single-scan dual-energy coded aperture computed tomography system that enables material characterization at a reduced exposure level. Rapid kVp switching with a single-static block/unblock coded aperture relies on coded illumination with a plurality of X-ray spectra created by the kVp switching. Based on the tensor representation of the projection data, an algorithm to estimate the missing measurements in the tensor is proposed. This results in a full set of synthesized measurements that can be used with filtered back-projection or iterative reconstruction algorithms to accurately reconstruct the object in each energy channel. Simulation results validate the effectiveness of the proposed cost-effective solution to attain material characterization in low-dose dual-energy CT.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134016849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-13DOI: 10.1109/ISBI48211.2021.9434089
J. Beaumont, O. Acosta, P. Raniga, G. Gambarota, J. Fripp
Brain morphometry performed with magnetic resonance (MR) imaging is affected by partial volume (PV) effects when single voxels contain the signal from two different tissues. This paper proposes a generalization of the MP2 RAGE sequence PV estimation model which accounts for transmitted magnetic field $(B1^{+})$ inhomogeneities at 7T. Our simulation experiments demonstrated that the PV estimation error of the proposed model is significantly lower than the error obtained with the same model neglecting $B1^{+}$ inhomogeneities (p<0.0001). The accuracy and precision of the $B1^{+}$ model (acc=92.0%, prec=89.6%) was significantly increased compared to the non $B1^{+}$ model (acc=69.8%, prec=65.4%). This highlights the importance of accounting for $B1^{+}$ inhomogeneities when computing PV on MP2RAGE data, which would otherwise limit the accuracy of brain morphometry at 7T.
{"title":"Towards a generalization of the MP2RAGE partial volume estimation model to account for B1+ inhomogeneities at 7T","authors":"J. Beaumont, O. Acosta, P. Raniga, G. Gambarota, J. Fripp","doi":"10.1109/ISBI48211.2021.9434089","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9434089","url":null,"abstract":"Brain morphometry performed with magnetic resonance (MR) imaging is affected by partial volume (PV) effects when single voxels contain the signal from two different tissues. This paper proposes a generalization of the MP2 RAGE sequence PV estimation model which accounts for transmitted magnetic field $(B1^{+})$ inhomogeneities at 7T. Our simulation experiments demonstrated that the PV estimation error of the proposed model is significantly lower than the error obtained with the same model neglecting $B1^{+}$ inhomogeneities (p<0.0001). The accuracy and precision of the $B1^{+}$ model (acc=92.0%, prec=89.6%) was significantly increased compared to the non $B1^{+}$ model (acc=69.8%, prec=65.4%). This highlights the importance of accounting for $B1^{+}$ inhomogeneities when computing PV on MP2RAGE data, which would otherwise limit the accuracy of brain morphometry at 7T.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133289248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-13DOI: 10.1109/ISBI48211.2021.9433773
Zishun Feng, J. Sivak, Ashok K. Krishnamurthy
There is considerable interest in AI systems that can assist a cardiologist to diagnose echocardiograms, and can also be used to train residents in classifying echocardiograms. Prior work has focused on the analysis of a single frame. Classifying echocardiograms at the video-level is challenging due to intra-frame and inter-frame noise. We propose a two-stream deep network which learns from the spatial context and optical flow for the classification of echocardiography videos. Each stream contains two parts: a Convolutional Neural Network (CNN) for spatial features and a bi-directional Long Short-Term Memory (LSTM) network with Attention for temporal. The features from these two streams are fused for classification. We verify our experimental results on a dataset of 170 (80 normal and 90 abnormal) videos that have been manually labeled by trained cardiologists. Our method provides an overall accuracy of 91.18%, with a sensitivity of 94.11% and a specificity of 88.24%.
{"title":"Two-Stream Attention Spatio-Temporal Network For Classification Of Echocardiography Videos","authors":"Zishun Feng, J. Sivak, Ashok K. Krishnamurthy","doi":"10.1109/ISBI48211.2021.9433773","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9433773","url":null,"abstract":"There is considerable interest in AI systems that can assist a cardiologist to diagnose echocardiograms, and can also be used to train residents in classifying echocardiograms. Prior work has focused on the analysis of a single frame. Classifying echocardiograms at the video-level is challenging due to intra-frame and inter-frame noise. We propose a two-stream deep network which learns from the spatial context and optical flow for the classification of echocardiography videos. Each stream contains two parts: a Convolutional Neural Network (CNN) for spatial features and a bi-directional Long Short-Term Memory (LSTM) network with Attention for temporal. The features from these two streams are fused for classification. We verify our experimental results on a dataset of 170 (80 normal and 90 abnormal) videos that have been manually labeled by trained cardiologists. Our method provides an overall accuracy of 91.18%, with a sensitivity of 94.11% and a specificity of 88.24%.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131750839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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