Pub Date : 2024-08-08DOI: 10.1007/s10278-024-01219-2
Usharani Bhimavarapu
Segmenting retinal blood vessels poses a significant challenge due to the irregularities inherent in small vessels. The complexity arises from the intricate task of effectively merging features at multiple levels, coupled with potential spatial information loss during successive down-sampling steps. This particularly affects the identification of small and faintly contrasting vessels. To address these challenges, we present a model tailored for automated arterial and venous (A/V) classification, complementing blood vessel segmentation. This paper presents an advanced methodology for segmenting and classifying retinal vessels using a series of sophisticated pre-processing and feature extraction techniques. The ensemble filter approach, incorporating Bilateral and Laplacian edge detectors, enhances image contrast and preserves edges. The proposed algorithm further refines the image by generating an orientation map. During the vessel extraction step, a complete convolution network processes the input image to create a detailed vessel map, enhanced by attention operations that improve modeling perception and resilience. The encoder extracts semantic features, while the Attention Module refines blood vessel depiction, resulting in highly accurate segmentation outcomes. The model was verified using the STARE dataset, which includes 400 images; the DRIVE dataset with 40 images; the HRF dataset with 45 images; and the INSPIRE-AVR dataset containing 40 images. The proposed model demonstrated superior performance across all datasets, achieving an accuracy of 97.5% on the DRIVE dataset, 99.25% on the STARE dataset, 98.33% on the INSPIREAVR dataset, and 98.67% on the HRF dataset. These results highlight the method's effectiveness in accurately segmenting and classifying retinal vessels.
{"title":"Retina Blood Vessels Segmentation and Classification with the Multi-featured Approach.","authors":"Usharani Bhimavarapu","doi":"10.1007/s10278-024-01219-2","DOIUrl":"https://doi.org/10.1007/s10278-024-01219-2","url":null,"abstract":"<p><p>Segmenting retinal blood vessels poses a significant challenge due to the irregularities inherent in small vessels. The complexity arises from the intricate task of effectively merging features at multiple levels, coupled with potential spatial information loss during successive down-sampling steps. This particularly affects the identification of small and faintly contrasting vessels. To address these challenges, we present a model tailored for automated arterial and venous (A/V) classification, complementing blood vessel segmentation. This paper presents an advanced methodology for segmenting and classifying retinal vessels using a series of sophisticated pre-processing and feature extraction techniques. The ensemble filter approach, incorporating Bilateral and Laplacian edge detectors, enhances image contrast and preserves edges. The proposed algorithm further refines the image by generating an orientation map. During the vessel extraction step, a complete convolution network processes the input image to create a detailed vessel map, enhanced by attention operations that improve modeling perception and resilience. The encoder extracts semantic features, while the Attention Module refines blood vessel depiction, resulting in highly accurate segmentation outcomes. The model was verified using the STARE dataset, which includes 400 images; the DRIVE dataset with 40 images; the HRF dataset with 45 images; and the INSPIRE-AVR dataset containing 40 images. The proposed model demonstrated superior performance across all datasets, achieving an accuracy of 97.5% on the DRIVE dataset, 99.25% on the STARE dataset, 98.33% on the INSPIREAVR dataset, and 98.67% on the HRF dataset. These results highlight the method's effectiveness in accurately segmenting and classifying retinal vessels.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908817","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 : 2024-08-08DOI: 10.1007/s10278-024-01211-w
Lening Li, Teng Zhang, Fan Lin, Yuting Li, Man-Sang Wong
To propose a deep learning framework "SpineCurve-net" for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical scoliosis patients. A total of 116 scoliosis patients were analyzed, divided into a training set of 89 patients (average age 32.4 ± 24.5 years) and a validation set of 27 patients (average age 17.3 ± 5.8 years). Vertebral identification and curve fitting were achieved through U-net and NURBS-net and resulted in a Non-Uniform Rational B-Spline (NURBS) curve of the spine. The 3D Cobb angles were measured in two ways: the predicted 3D Cobb angle (PRED-3D-CA), which is the maximum value in the smoothed angle map derived from the NURBS curve, and the 2D mapping Cobb angle (MAP-2D-CA), which is the maximal angle formed by the tangent vectors along the projected 2D spinal curve. The model segmented spinal masks effectively, capturing easily missed vertebral bodies. Spoke kernel filtering distinguished vertebral regions, centralizing spinal curves. The SpineCurve Network method's Cobb angle (PRED-3D-CA and MAP-2D-CA) measurements correlated strongly with the surgeons' annotated Cobb angle (ground truth, GT) based on 2D radiographs, revealing high Pearson correlation coefficients of 0.983 and 0.934, respectively. This paper proposed an automated technique for calculating the 3D Cobb angle in preoperative scoliosis patients, yielding results that are highly correlated with traditional 2D Cobb angle measurements. Given its capacity to accurately represent the three-dimensional nature of spinal deformities, this method shows potential in aiding physicians to develop more precise surgical strategies in upcoming cases.
{"title":"Automated 3D Cobb Angle Measurement Using U-Net in CT Images of Preoperative Scoliosis Patients.","authors":"Lening Li, Teng Zhang, Fan Lin, Yuting Li, Man-Sang Wong","doi":"10.1007/s10278-024-01211-w","DOIUrl":"https://doi.org/10.1007/s10278-024-01211-w","url":null,"abstract":"<p><p>To propose a deep learning framework \"SpineCurve-net\" for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical scoliosis patients. A total of 116 scoliosis patients were analyzed, divided into a training set of 89 patients (average age 32.4 ± 24.5 years) and a validation set of 27 patients (average age 17.3 ± 5.8 years). Vertebral identification and curve fitting were achieved through U-net and NURBS-net and resulted in a Non-Uniform Rational B-Spline (NURBS) curve of the spine. The 3D Cobb angles were measured in two ways: the predicted 3D Cobb angle (PRED-3D-CA), which is the maximum value in the smoothed angle map derived from the NURBS curve, and the 2D mapping Cobb angle (MAP-2D-CA), which is the maximal angle formed by the tangent vectors along the projected 2D spinal curve. The model segmented spinal masks effectively, capturing easily missed vertebral bodies. Spoke kernel filtering distinguished vertebral regions, centralizing spinal curves. The SpineCurve Network method's Cobb angle (PRED-3D-CA and MAP-2D-CA) measurements correlated strongly with the surgeons' annotated Cobb angle (ground truth, GT) based on 2D radiographs, revealing high Pearson correlation coefficients of 0.983 and 0.934, respectively. This paper proposed an automated technique for calculating the 3D Cobb angle in preoperative scoliosis patients, yielding results that are highly correlated with traditional 2D Cobb angle measurements. Given its capacity to accurately represent the three-dimensional nature of spinal deformities, this method shows potential in aiding physicians to develop more precise surgical strategies in upcoming cases.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908816","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 : 2024-08-06DOI: 10.1007/s10278-024-01217-4
Bincheng Peng, Chao Fan
Currently, deep learning is developing rapidly in the field of image segmentation, and medical image segmentation is one of the key applications in this field. Conventional CNN has achieved great success in general medical image segmentation tasks, but it has feature loss in the feature extraction part and lacks the ability to explicitly model remote dependencies, which makes it difficult to adapt to the task of human organ segmentation. Although methods containing attention mechanisms have made good progress in the field of semantic segmentation, most of the current attention mechanisms are limited to a single sample, while the number of samples of human organ images is large, ignoring the correlation between the samples is not conducive to image segmentation. In order to solve these problems, an internal and external dual-attention segmentation network (IEA-Net) is proposed in this paper, and the ICSwR (interleaved convolutional system with residual) module and the IEAM module are designed in this network. The ICSwR contains interleaved convolution and hopping connection, which are used for the initial extraction of the features in the encoder part. The IEAM module (internal and external dual-attention module) consists of the LGGW-SA (local-global Gaussian-weighted self-attention) module and the EA module, which are in a tandem structure. The LGGW-SA module focuses on learning local-global feature correlations within individual samples for efficient feature extraction. Meanwhile, the EA module is designed to capture inter-sample connections, addressing multi-sample complexities. Additionally, skip connections will be incorporated into each IEAM module within both the encoder and decoder to reduce feature loss. We tested our method on the Synapse multi-organ segmentation dataset and the ACDC cardiac segmentation dataset, and the experimental results show that the proposed method achieves better performance than other state-of-the-art methods.
{"title":"IEA-Net: Internal and External Dual-Attention Medical Segmentation Network with High-Performance Convolutional Blocks.","authors":"Bincheng Peng, Chao Fan","doi":"10.1007/s10278-024-01217-4","DOIUrl":"https://doi.org/10.1007/s10278-024-01217-4","url":null,"abstract":"<p><p>Currently, deep learning is developing rapidly in the field of image segmentation, and medical image segmentation is one of the key applications in this field. Conventional CNN has achieved great success in general medical image segmentation tasks, but it has feature loss in the feature extraction part and lacks the ability to explicitly model remote dependencies, which makes it difficult to adapt to the task of human organ segmentation. Although methods containing attention mechanisms have made good progress in the field of semantic segmentation, most of the current attention mechanisms are limited to a single sample, while the number of samples of human organ images is large, ignoring the correlation between the samples is not conducive to image segmentation. In order to solve these problems, an internal and external dual-attention segmentation network (IEA-Net) is proposed in this paper, and the ICSwR (interleaved convolutional system with residual) module and the IEAM module are designed in this network. The ICSwR contains interleaved convolution and hopping connection, which are used for the initial extraction of the features in the encoder part. The IEAM module (internal and external dual-attention module) consists of the LGGW-SA (local-global Gaussian-weighted self-attention) module and the EA module, which are in a tandem structure. The LGGW-SA module focuses on learning local-global feature correlations within individual samples for efficient feature extraction. Meanwhile, the EA module is designed to capture inter-sample connections, addressing multi-sample complexities. Additionally, skip connections will be incorporated into each IEAM module within both the encoder and decoder to reduce feature loss. We tested our method on the Synapse multi-organ segmentation dataset and the ACDC cardiac segmentation dataset, and the experimental results show that the proposed method achieves better performance than other state-of-the-art methods.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141895162","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 : 2024-08-05DOI: 10.1007/s10278-024-01208-5
Yeong Hun Kang, Jin Youp Kim, Young Jae Kim, Sung Hyun Kim, Kwang Gi Kim, Chae-Seo Rhee
Obstructive sleep apnea is characterized by a decrease or cessation of breathing due to repetitive closure of the upper airway during sleep, leading to a decrease in blood oxygen saturation. In this study, employing a U-Net model, we utilized drug-induced sleep endoscopy images to segment the major causes of airway obstruction, including the epiglottis, oropharynx lateral walls, and tongue base. The evaluation metrics included sensitivity, specificity, accuracy, and Dice score, with airway sensitivity at 0.93 (± 0.06), specificity at 0.96 (± 0.01), accuracy at 0.95 (± 0.01), and Dice score at 0.84 (± 0.03), indicating overall high performance. The results indicate the potential for artificial intelligence (AI)-driven automatic interpretation of sleep disorder diagnosis, with implications for standardizing medical procedures and improving healthcare services. The study suggests that advancements in AI technology hold promise for enhancing diagnostic accuracy and treatment efficacy in sleep and respiratory disorders, fostering competitiveness in the medical AI market.
{"title":"Airway and Airway Obstruction Site Segmentation Study Using U-Net with Drug-Induced Sleep Endoscopy Images.","authors":"Yeong Hun Kang, Jin Youp Kim, Young Jae Kim, Sung Hyun Kim, Kwang Gi Kim, Chae-Seo Rhee","doi":"10.1007/s10278-024-01208-5","DOIUrl":"https://doi.org/10.1007/s10278-024-01208-5","url":null,"abstract":"<p><p>Obstructive sleep apnea is characterized by a decrease or cessation of breathing due to repetitive closure of the upper airway during sleep, leading to a decrease in blood oxygen saturation. In this study, employing a U-Net model, we utilized drug-induced sleep endoscopy images to segment the major causes of airway obstruction, including the epiglottis, oropharynx lateral walls, and tongue base. The evaluation metrics included sensitivity, specificity, accuracy, and Dice score, with airway sensitivity at 0.93 (± 0.06), specificity at 0.96 (± 0.01), accuracy at 0.95 (± 0.01), and Dice score at 0.84 (± 0.03), indicating overall high performance. The results indicate the potential for artificial intelligence (AI)-driven automatic interpretation of sleep disorder diagnosis, with implications for standardizing medical procedures and improving healthcare services. The study suggests that advancements in AI technology hold promise for enhancing diagnostic accuracy and treatment efficacy in sleep and respiratory disorders, fostering competitiveness in the medical AI market.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141895159","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 : 2024-08-05DOI: 10.1007/s10278-024-01203-w
Mana Moassefi
{"title":"My Experience with Academy - Advancing Innovation in Imaging.","authors":"Mana Moassefi","doi":"10.1007/s10278-024-01203-w","DOIUrl":"https://doi.org/10.1007/s10278-024-01203-w","url":null,"abstract":"","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141895163","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 : 2024-08-05DOI: 10.1007/s10278-024-01210-x
Minho Choi, Jun-Su Jang
Medical staff inspect lumbar X-ray images to diagnose lumbar spine diseases, and the analysis process is currently automated using deep-learning techniques. The detection of landmarks is necessary in the automatic process of localizing the position and identifying the morphological features of the vertebrae. However, detection errors may occur owing to the noise and ambiguity of images, as well as individual variations in the shape of the lumbar vertebrae. This study proposes a method to improve the robustness of landmark detection results. This method assumes that landmarks are detected by a convolutional neural network-based two-step model consisting of Pose-Net and M-Net. The model generates a heatmap response to indicate the probable landmark positions. The proposed method then corrects the landmark positions using the heatmap response and active shape model, which employs statistical information on the landmark distribution. Experiments were conducted using 3600 lumbar X-ray images, and the results showed that the landmark detection error was reduced by the proposed method. The average value of maximum errors decreased by 5.58% after applying the proposed method, which combines the outstanding image analysis capabilities of deep learning with statistical shape constraints on landmark distribution. The proposed method could also be easily integrated with other techniques to increase the robustness of landmark detection results such as CoordConv layers and non-directional part affinity field. This resulted in a further enhancement in the landmark detection performance. These advantages can improve the reliability of automatic systems used to inspect lumbar X-ray images. This will benefit both patients and medical staff by reducing medical expenses and increasing diagnostic efficiency.
医务人员通过检查腰椎 X 射线图像来诊断腰椎疾病,目前分析过程已利用深度学习技术实现自动化。在定位椎体位置和识别椎体形态特征的自动过程中,地标检测是必要的。然而,由于图像的噪声和模糊性,以及腰椎形状的个体差异,可能会出现检测错误。本研究提出了一种提高地标检测结果稳健性的方法。该方法假设地标由基于卷积神经网络的两步模型检测,该模型由 Pose-Net 和 M-Net 组成。该模型生成热图响应,以指示可能的地标位置。然后,建议的方法利用热图响应和主动形状模型修正地标位置,主动形状模型采用了地标分布的统计信息。实验使用了 3600 张腰椎 X 光图像,结果表明,所提出的方法减少了地标检测误差。该方法结合了深度学习出色的图像分析能力和对地标分布的统计形状约束,应用该方法后,最大误差的平均值降低了 5.58%。所提出的方法还可以轻松地与其他技术相结合,以提高地标检测结果的鲁棒性,如 CoordConv 层和非定向部分亲和场。这进一步提高了地标检测性能。这些优势可以提高用于检测腰椎 X 光图像的自动系统的可靠性。这将降低医疗费用,提高诊断效率,从而使患者和医务人员受益。
{"title":"Heatmap-Based Active Shape Model for Landmark Detection in Lumbar X-ray Images.","authors":"Minho Choi, Jun-Su Jang","doi":"10.1007/s10278-024-01210-x","DOIUrl":"https://doi.org/10.1007/s10278-024-01210-x","url":null,"abstract":"<p><p>Medical staff inspect lumbar X-ray images to diagnose lumbar spine diseases, and the analysis process is currently automated using deep-learning techniques. The detection of landmarks is necessary in the automatic process of localizing the position and identifying the morphological features of the vertebrae. However, detection errors may occur owing to the noise and ambiguity of images, as well as individual variations in the shape of the lumbar vertebrae. This study proposes a method to improve the robustness of landmark detection results. This method assumes that landmarks are detected by a convolutional neural network-based two-step model consisting of Pose-Net and M-Net. The model generates a heatmap response to indicate the probable landmark positions. The proposed method then corrects the landmark positions using the heatmap response and active shape model, which employs statistical information on the landmark distribution. Experiments were conducted using 3600 lumbar X-ray images, and the results showed that the landmark detection error was reduced by the proposed method. The average value of maximum errors decreased by 5.58% after applying the proposed method, which combines the outstanding image analysis capabilities of deep learning with statistical shape constraints on landmark distribution. The proposed method could also be easily integrated with other techniques to increase the robustness of landmark detection results such as CoordConv layers and non-directional part affinity field. This resulted in a further enhancement in the landmark detection performance. These advantages can improve the reliability of automatic systems used to inspect lumbar X-ray images. This will benefit both patients and medical staff by reducing medical expenses and increasing diagnostic efficiency.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141895161","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 : 2024-08-05DOI: 10.1007/s10278-024-01207-6
Pengfei Cai, Biyuan Li, Gaowei Sun, Bo Yang, Xiuwei Wang, Chunjie Lv, Jun Yan
Retinal vessel segmentation is crucial for the diagnosis of ophthalmic and cardiovascular diseases. However, retinal vessels are densely and irregularly distributed, with many capillaries blending into the background, and exhibit low contrast. Moreover, the encoder-decoder-based network for retinal vessel segmentation suffers from irreversible loss of detailed features due to multiple encoding and decoding, leading to incorrect segmentation of the vessels. Meanwhile, the single-dimensional attention mechanisms possess limitations, neglecting the importance of multidimensional features. To solve these issues, in this paper, we propose a detail-enhanced attention feature fusion network (DEAF-Net) for retinal vessel segmentation. First, the detail-enhanced residual block (DERB) module is proposed to strengthen the capacity for detailed representation, ensuring that intricate features are efficiently maintained during the segmentation of delicate vessels. Second, the multidimensional collaborative attention encoder (MCAE) module is proposed to optimize the extraction of multidimensional information. Then, the dynamic decoder (DYD) module is introduced to preserve spatial information during the decoding process and reduce the information loss caused by upsampling operations. Finally, the proposed detail-enhanced feature fusion (DEFF) module composed of DERB, MCAE and DYD modules fuses feature maps from both encoding and decoding and achieves effective aggregation of multi-scale contextual information. The experiments conducted on the datasets of DRIVE, CHASEDB1, and STARE, achieving Sen of 0.8305, 0.8784, and 0.8654, and AUC of 0.9886, 0.9913, and 0.9911 on DRIVE, CHASEDB1, and STARE, respectively, demonstrate the performance of our proposed network, particularly in the segmentation of fine retinal vessels.
视网膜血管分割对眼科和心血管疾病的诊断至关重要。然而,视网膜血管分布密集且不规则,许多毛细血管与背景融为一体,而且对比度低。此外,基于编码器-解码器的视网膜血管分割网络会因多次编码和解码而不可逆转地丢失细节特征,导致血管分割错误。同时,单维注意力机制存在局限性,忽略了多维特征的重要性。为了解决这些问题,本文提出了一种用于视网膜血管分割的细节增强注意力特征融合网络(DEAF-Net)。首先,我们提出了细节增强残差块(DERB)模块,以加强细节表示能力,确保在分割精细血管时有效保留复杂特征。其次,提出了多维协同注意编码器(MCAE)模块,以优化多维信息的提取。然后,引入动态解码器(DYD)模块,在解码过程中保留空间信息,减少上采样操作造成的信息损失。最后,由 DERB、MCAE 和 DYD 模块组成的细节增强特征融合(DEFF)模块融合了编码和解码的特征图,实现了多尺度上下文信息的有效聚合。在 DRIVE、CHASEDB1 和 STARE 数据集上进行的实验证明了我们所提出的网络的性能,尤其是在细小视网膜血管的分割上,其 Sen 值分别达到了 0.8305、0.8784 和 0.8654,AUC 值分别达到了 0.9886、0.9913 和 0.9911。
{"title":"DEAF-Net: Detail-Enhanced Attention Feature Fusion Network for Retinal Vessel Segmentation.","authors":"Pengfei Cai, Biyuan Li, Gaowei Sun, Bo Yang, Xiuwei Wang, Chunjie Lv, Jun Yan","doi":"10.1007/s10278-024-01207-6","DOIUrl":"https://doi.org/10.1007/s10278-024-01207-6","url":null,"abstract":"<p><p>Retinal vessel segmentation is crucial for the diagnosis of ophthalmic and cardiovascular diseases. However, retinal vessels are densely and irregularly distributed, with many capillaries blending into the background, and exhibit low contrast. Moreover, the encoder-decoder-based network for retinal vessel segmentation suffers from irreversible loss of detailed features due to multiple encoding and decoding, leading to incorrect segmentation of the vessels. Meanwhile, the single-dimensional attention mechanisms possess limitations, neglecting the importance of multidimensional features. To solve these issues, in this paper, we propose a detail-enhanced attention feature fusion network (DEAF-Net) for retinal vessel segmentation. First, the detail-enhanced residual block (DERB) module is proposed to strengthen the capacity for detailed representation, ensuring that intricate features are efficiently maintained during the segmentation of delicate vessels. Second, the multidimensional collaborative attention encoder (MCAE) module is proposed to optimize the extraction of multidimensional information. Then, the dynamic decoder (DYD) module is introduced to preserve spatial information during the decoding process and reduce the information loss caused by upsampling operations. Finally, the proposed detail-enhanced feature fusion (DEFF) module composed of DERB, MCAE and DYD modules fuses feature maps from both encoding and decoding and achieves effective aggregation of multi-scale contextual information. The experiments conducted on the datasets of DRIVE, CHASEDB1, and STARE, achieving Sen of 0.8305, 0.8784, and 0.8654, and AUC of 0.9886, 0.9913, and 0.9911 on DRIVE, CHASEDB1, and STARE, respectively, demonstrate the performance of our proposed network, particularly in the segmentation of fine retinal vessels.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141895160","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 : 2024-08-01Epub Date: 2024-02-21DOI: 10.1007/s10278-024-01020-1
Jong Chan Yeom, Jae Hoon Kim, Young Jae Kim, Jisup Kim, Kwang Gi Kim
Federated learning, an innovative artificial intelligence training method, offers a secure solution for institutions to collaboratively develop models without sharing raw data. This approach offers immense promise and is particularly advantageous for domains dealing with sensitive information, such as patient data. However, when confronted with a distributed data environment, challenges arise due to data paucity or inherent heterogeneity, potentially impacting the performance of federated learning models. Hence, scrutinizing the efficacy of this method in such intricate settings is indispensable. To address this, we harnessed pathological image datasets of endometrial cancer from four hospitals for training and evaluating the performance of a federated learning model and compared it with a centralized learning model. With optimal processing techniques (data augmentation, color normalization, and adaptive optimizer), federated learning exhibited lower precision but higher recall and Dice similarity coefficient (DSC) than centralized learning. Hence, considering the critical importance of recall in the context of medical image processing, federated learning is demonstrated as a viable and applicable approach in this field, offering advantages in terms of both performance and data security.
{"title":"A Comparative Study of Performance Between Federated Learning and Centralized Learning Using Pathological Image of Endometrial Cancer.","authors":"Jong Chan Yeom, Jae Hoon Kim, Young Jae Kim, Jisup Kim, Kwang Gi Kim","doi":"10.1007/s10278-024-01020-1","DOIUrl":"10.1007/s10278-024-01020-1","url":null,"abstract":"<p><p>Federated learning, an innovative artificial intelligence training method, offers a secure solution for institutions to collaboratively develop models without sharing raw data. This approach offers immense promise and is particularly advantageous for domains dealing with sensitive information, such as patient data. However, when confronted with a distributed data environment, challenges arise due to data paucity or inherent heterogeneity, potentially impacting the performance of federated learning models. Hence, scrutinizing the efficacy of this method in such intricate settings is indispensable. To address this, we harnessed pathological image datasets of endometrial cancer from four hospitals for training and evaluating the performance of a federated learning model and compared it with a centralized learning model. With optimal processing techniques (data augmentation, color normalization, and adaptive optimizer), federated learning exhibited lower precision but higher recall and Dice similarity coefficient (DSC) than centralized learning. Hence, considering the critical importance of recall in the context of medical image processing, federated learning is demonstrated as a viable and applicable approach in this field, offering advantages in terms of both performance and data security.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11300724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139914396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01Epub Date: 2024-02-20DOI: 10.1007/s10278-024-00979-1
Jianning Chi, Xiaolin Wei, Zhiyi Sun, Yongming Yang, Bin Yang
Low-dose computed tomography (LDCT) has been widely used in medical diagnosis. In practice, doctors often zoom in on LDCT slices for clearer lesions and issues, while, a simple zooming operation fails to suppress low-dose artifacts, leading to distorted details. Therefore, numerous LDCT super-resolution (SR) methods have been proposed to promote the quality of zooming without the increase of the dose in CT scanning. However, there are still some drawbacks that need to be addressed in existing methods. First, the region of interest (ROI) is not emphasized due to the lack of guidance in the reconstruction process. Second, the convolutional blocks extracting fix-resolution features fail to concentrate on the essential multi-scale features. Third, a single SR head cannot suppress the residual artifacts. To address these issues, we propose an LDCT CT joint SR and denoising reconstruction network. Our proposed network consists of global dual-guidance attention fusion modules (GDAFMs) and multi-scale anastomosis blocks (MABs). The GDAFM directs the network to focus on ROI by fusing the extra mask guidance and average CT image guidance, while the MAB introduces hierarchical features through anastomosis connections to leverage multi-scale features and promote the feature representation ability. To suppress radial residual artifacts, we optimize our network using the feedback feature distillation mechanism (FFDM) which shares the backbone to learn features corresponding to the denoising task. We apply the proposed method to the 3D-IRCADB and PANCREAS datasets to evaluate its ability on LDCT image SR reconstruction. The experimental results compared with state-of-the-art methods illustrate the superiority of our approach with respect to peak signal-to-noise (PSNR), structural similarity (SSIM), and qualitative observations. Our proposed LDCT joint SR and denoising reconstruction network has been extensively evaluated through ablation, quantitative, and qualitative experiments. The results demonstrate that our method can recover noise-free and detail-sharp images, resulting in better reconstruction results. Code is available at https://github.com/neu-szy/ldct_sr_dn_w_ffdm .
低剂量计算机断层扫描(LDCT)已广泛应用于医学诊断。在实践中,医生经常会放大 LDCT 切片,以获得更清晰的病变和问题,而简单的放大操作无法抑制低剂量伪影,导致细节失真。因此,人们提出了许多 LDCT 超分辨率(SR)方法,以在不增加 CT 扫描剂量的情况下提高缩放质量。然而,现有方法仍有一些缺点需要解决。首先,由于在重建过程中缺乏指导,感兴趣区域(ROI)没有得到强调。其次,提取固定分辨率特征的卷积块不能集中在重要的多尺度特征上。第三,单个 SR 头无法抑制残留伪影。为了解决这些问题,我们提出了一种 LDCT CT 联合 SR 和去噪重建网络。我们提出的网络由全局双导向注意力融合模块(GDAFM)和多尺度吻合块(MAB)组成。GDAFM 通过融合额外掩膜引导和平均 CT 图像引导,引导网络聚焦于 ROI,而 MAB 则通过吻合连接引入分层特征,以利用多尺度特征并提高特征表示能力。为了抑制径向残留伪影,我们使用反馈特征蒸馏机制(FFDM)优化网络,该机制共享骨干网,以学习与去噪任务相对应的特征。我们将所提出的方法应用于 3D-IRCADB 和 PANCREAS 数据集,以评估其在 LDCT 图像 SR 重建方面的能力。与最先进的方法相比,实验结果表明我们的方法在峰值信噪比(PSNR)、结构相似性(SSIM)和定性观察方面更胜一筹。我们提出的 LDCT 联合 SR 和去噪重建网络已通过消融、定量和定性实验进行了广泛评估。结果表明,我们的方法可以恢复无噪声和细节清晰的图像,从而获得更好的重建效果。代码见 https://github.com/neu-szy/ldct_sr_dn_w_ffdm 。
{"title":"Low-Dose CT Image Super-resolution Network with Noise Inhibition Based on Feedback Feature Distillation Mechanism.","authors":"Jianning Chi, Xiaolin Wei, Zhiyi Sun, Yongming Yang, Bin Yang","doi":"10.1007/s10278-024-00979-1","DOIUrl":"10.1007/s10278-024-00979-1","url":null,"abstract":"<p><p>Low-dose computed tomography (LDCT) has been widely used in medical diagnosis. In practice, doctors often zoom in on LDCT slices for clearer lesions and issues, while, a simple zooming operation fails to suppress low-dose artifacts, leading to distorted details. Therefore, numerous LDCT super-resolution (SR) methods have been proposed to promote the quality of zooming without the increase of the dose in CT scanning. However, there are still some drawbacks that need to be addressed in existing methods. First, the region of interest (ROI) is not emphasized due to the lack of guidance in the reconstruction process. Second, the convolutional blocks extracting fix-resolution features fail to concentrate on the essential multi-scale features. Third, a single SR head cannot suppress the residual artifacts. To address these issues, we propose an LDCT CT joint SR and denoising reconstruction network. Our proposed network consists of global dual-guidance attention fusion modules (GDAFMs) and multi-scale anastomosis blocks (MABs). The GDAFM directs the network to focus on ROI by fusing the extra mask guidance and average CT image guidance, while the MAB introduces hierarchical features through anastomosis connections to leverage multi-scale features and promote the feature representation ability. To suppress radial residual artifacts, we optimize our network using the feedback feature distillation mechanism (FFDM) which shares the backbone to learn features corresponding to the denoising task. We apply the proposed method to the 3D-IRCADB and PANCREAS datasets to evaluate its ability on LDCT image SR reconstruction. The experimental results compared with state-of-the-art methods illustrate the superiority of our approach with respect to peak signal-to-noise (PSNR), structural similarity (SSIM), and qualitative observations. Our proposed LDCT joint SR and denoising reconstruction network has been extensively evaluated through ablation, quantitative, and qualitative experiments. The results demonstrate that our method can recover noise-free and detail-sharp images, resulting in better reconstruction results. Code is available at https://github.com/neu-szy/ldct_sr_dn_w_ffdm .</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11300784/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139914403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01Epub Date: 2024-02-29DOI: 10.1007/s10278-024-01059-0
Ming Cheng, Hanyue Zhang, Wenpeng Huang, Fei Li, Jianbo Gao
This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn's disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on the validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learning (DL) radiomics features were extracted from the arterial and venous phases images, respectively. The interobserver consistency analysis, Spearman's correlation, univariate analysis, and the least absolute shrinkage and selection operator (LASSO) regression were used to select features. Based on extracted multi-phase radiomics features, six logistic regression models were finally constructed. The diagnostic performances of different models were compared using ROC analysis and Delong test. The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality with AUCs of 0.885, 0.877, and 0.800 in SMOTE dataset, validation dataset one, and validation dataset two, respectively. Moreover, the deep learning radiomics model outperformed the handcrafted radiomics model in same phase images. In validation dataset one, the Delong test results indicated that there was a significant difference in the AUC of the arterial models (p = 0.037), while not in venous and arterial-venous combined models (p = 0.398 and p = 0.265) as comparing deep learning radiomics models and handcrafted radiomics models. In our study, the arterial-venous combined model based on deep learning radiomics analysis exhibited good performance in differentiating between CD and ITB.
{"title":"Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn's Disease and Intestinal Tuberculosis.","authors":"Ming Cheng, Hanyue Zhang, Wenpeng Huang, Fei Li, Jianbo Gao","doi":"10.1007/s10278-024-01059-0","DOIUrl":"10.1007/s10278-024-01059-0","url":null,"abstract":"<p><p>This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn's disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on the validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learning (DL) radiomics features were extracted from the arterial and venous phases images, respectively. The interobserver consistency analysis, Spearman's correlation, univariate analysis, and the least absolute shrinkage and selection operator (LASSO) regression were used to select features. Based on extracted multi-phase radiomics features, six logistic regression models were finally constructed. The diagnostic performances of different models were compared using ROC analysis and Delong test. The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality with AUCs of 0.885, 0.877, and 0.800 in SMOTE dataset, validation dataset one, and validation dataset two, respectively. Moreover, the deep learning radiomics model outperformed the handcrafted radiomics model in same phase images. In validation dataset one, the Delong test results indicated that there was a significant difference in the AUC of the arterial models (p = 0.037), while not in venous and arterial-venous combined models (p = 0.398 and p = 0.265) as comparing deep learning radiomics models and handcrafted radiomics models. In our study, the arterial-venous combined model based on deep learning radiomics analysis exhibited good performance in differentiating between CD and ITB.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11300798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139998779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}