2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)最新文献

A Feature Regularization Based Meta-Learning Framework for Generalizing Prostate Mri Segmentation 基于特征正则化的元学习框架泛化前列腺Mri分割

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761564

Hui Wang, Zeyu Zhang, Bo Zhang, Y. Mi, Jingyun Wu, Haiwen Huang, Zibo Ma, Wendong Wang

引用次数: 3

Intracranial Vessel Wall Segmentation with Deep Learning Using a Novel Tiered Loss Function to Incorporate Class Inclusion 基于分层损失函数的深度学习颅内血管壁分割

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761428

Hanyue Zhou, Jiayu Xiao, Debiao Li, Z. Fan, D. Ruan

{"title":"Intracranial Vessel Wall Segmentation with Deep Learning Using a Novel Tiered Loss Function to Incorporate Class Inclusion","authors":"Hanyue Zhou, Jiayu Xiao, Debiao Li, Z. Fan, D. Ruan","doi":"10.1109/ISBI52829.2022.9761428","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761428","url":null,"abstract":"The goal of this study is to develop an automated vessel wall segmentation method on T1-weighted intracranial vessel wall magnetic resonance images, with a focus on modeling the inclusion relation between the inner and outer boundaries of the vessel wall. We propose a novel method that estimates the inner and outer vessel wall boundaries simultaneously, using a network with a single output channel resembling the level-set function height. The network is driven by a unique tiered loss that accounts for data fidelity of the lumen and vessel wall classes and a length regularization to encourage boundary smoothness. The proposed method achieved Dice similarity coefficients (DSC) in 2D of 0.925 ± 0.048, 0.786 ± 0.084, Hausdorff distance (HD) of 0.286 ± 0.436 mm, 0.345 ± 0.419 mm, and mean surface distance (MSD) of 0.083 ± 0.037 mm and 0.103 ± 0.032 mm for the lumen and vessel wall, respectively, on a test set; compared favorably to a benchmark UNet model that achieved DSC 0.924 ± 0.047, 0.794 ± 0.082, HD 0.298 ± 0.477 mm, 0.394 ± 0.431 mm, and MSD 0.087 ± 0.056 mm, 0.119 ± 0.059 mm.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"18 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74120415","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}

引用次数: 1

Leveraging Clinically Relevant Biometric Constraints to Supervise a Deep Learning Model for the Accurate Caliper Placement to Obtain Sonographic Measurements of the Fetal Brain 利用临床相关的生物特征约束来监督一个深度学习模型，用于精确的卡尺放置，以获得胎儿大脑的超声测量

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761493

H. Shankar, Ashwin Narayan, S. Jain, D. Singh, P. Vyas, N. Hegde, P. Kar, A. Lad, J. Thang, J. Atada, D. Nguyen, PS Roopa, A. Vasudeva, P. Radhakrishnan, S. Devalla

{"title":"Leveraging Clinically Relevant Biometric Constraints to Supervise a Deep Learning Model for the Accurate Caliper Placement to Obtain Sonographic Measurements of the Fetal Brain","authors":"H. Shankar, Ashwin Narayan, S. Jain, D. Singh, P. Vyas, N. Hegde, P. Kar, A. Lad, J. Thang, J. Atada, D. Nguyen, PS Roopa, A. Vasudeva, P. Radhakrishnan, S. Devalla","doi":"10.1109/ISBI52829.2022.9761493","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761493","url":null,"abstract":"Multiple studies have demonstrated that obtaining standardized fetal brain biometry from mid-trimester ultrasonography (USG) examination is key for the reliable assessment of fetal neurodevelopment and the screening of central nervous system (CNS) anomalies. Obtaining these measurements is highly subjective, expertise-driven, and requires years of training experience, limiting quality prenatal care for all pregnant mothers. In this study, we propose a deep learning (DL) approach to compute 3 key fetal brain biometry from the 2D USG images of the transcerebellar plane (TC) through the accurate and automated caliper placement (2 per biometry) by modeling it as a landmark detection problem. We leveraged clinically relevant biometric constraints (relationship between caliper points) and domain-relevant data augmentation to improve the accuracy of a U-Net DL model (trained/tested on: 596 images, 473 subjects/143 images, 143 subjects). We performed multiple experiments demonstrating the effect of the DL backbone, data augmentation, generalizability and benchmarked against a recent state-of-the-art approach through extensive clinical validation (DL vs. 7 experienced clinicians). For all cases, the mean errors in the placement of the individual caliper points and the computed biometry were comparable to error rates among clinicians. The clinical translation of the proposed framework can assist novice users from low-resource settings in the reliable and standardized assessment of fetal brain sonograms.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"62 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75417341","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}

引用次数: 0

Attention-Based Deep Multiple Instance Learning with Adaptive Instance Sampling 基于注意力的深度多实例学习与自适应实例采样

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761661

A. Tarkhan, Trung-Kien Nguyen, N. Simon, T. Bengtsson, Paolo Ocampo, Jian Dai

引用次数: 4

Accessible, Affordable and Low-Risk Lungs Health Monitoring in Covid-19: Deep Cascade Reconstruction from Degraded LR-ULDCT Covid-19中可获得、可负担和低风险的肺部健康监测:降解LR-ULDCT的深级联重建

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761566

Swati Rai, Jignesh S. Bhatt, S. K. Patra

{"title":"Accessible, Affordable and Low-Risk Lungs Health Monitoring in Covid-19: Deep Cascade Reconstruction from Degraded LR-ULDCT","authors":"Swati Rai, Jignesh S. Bhatt, S. K. Patra","doi":"10.1109/ISBI52829.2022.9761566","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761566","url":null,"abstract":"We present deep cascade reconstruction of degraded low-resolution ultra-low-dose computed tomography (LR-ULDCT) chest images to restored and super-resolved (SR) ULDCT as accessible, affordable, and relatively less hazardous recourse for lungs health monitoring in COVID-19; when compared to relatively less available, costly, and high radiation dose high-resolution CT (HRCT). The degraded LR-ULDCT is first restored with unsupervised dictionary-based deep residual learning network that handles degradations along with Poisson noise found in CT data. The restored version is given to SR network that increases its spatial resolution by minimizing adversarial loss between LR-ULDCT and reconstructed SR-ULDCT within minimax game. It is then fed for segmentation which is achieved by additional block of convolution, Leaky-ReLU, and batch-normalization in U-Net. Thus restored segmented SR-ULDCT estimates presence of ground glass opacity and facilitates monitoring of lungs health at par HRCT. Comparative experiments and ablation study are presented using synthetic and real COVID-19 data.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"94 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79450638","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}

引用次数: 1

Decouple-Couple Network for Drug-Resistant EGFR Mutation Subtype Prediction with Lung Cancer CT Images 解耦耦网络用于肺癌CT图像耐药EGFR突变亚型预测

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761599

Yongbei Zhu, Liusu Wang, He Yu, Meili Liu, Mingyu Zhang, Wei-min Li, Shuo Wang, Jie Tian

{"title":"Decouple-Couple Network for Drug-Resistant EGFR Mutation Subtype Prediction with Lung Cancer CT Images","authors":"Yongbei Zhu, Liusu Wang, He Yu, Meili Liu, Mingyu Zhang, Wei-min Li, Shuo Wang, Jie Tian","doi":"10.1109/ISBI52829.2022.9761599","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761599","url":null,"abstract":"Epidermal growth factor receptor (EGFR)-targeted therapy has revolutionized the treatment of EGFR-mutant lung cancer. However, a part of patients (nearly 10%) with mutated EGFR harbor drug-resistant mutation (DRM) subtypes. Although computed tomography images and deep learning have shown promising results in non-invasively predicting EGFR genotype, which may not be suitable to identify the DRM subtypes due to the imbalanced data distribution and the intra-class diversity of majority class. Hence, we propose a novel decouple-couple network (DCNet) to identify the DRM subtypes. Our DCNet firstly decouples the features of majority class as multiple prototypes, and then couple the prototypes of each class as one prototype for further classification. Meanwhile, the decouple-couple procedure is optimized jointly based on updated similarity score and prototypical contrastive learning. Furthermore, we collect a large CT dataset including 1232 EGFR-mutant lung cancer patients and the DCNet achieved sensitivity over 0.6, which improves largely than the state-of-the-art methods.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"12 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81922087","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}

引用次数: 0

Hybrid Attentive Unet for Segmentation of Lower Leg Muscles and Bones From MRI Scans For Musculoskeletal Research 用于肌肉骨骼研究的从MRI扫描中分割小腿肌肉和骨骼的混合注意单元

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761501

Jiayi Zhu, Bart Bolsterlee, Brian V. Y. Chow, Yang Song, E. Meijering

{"title":"Hybrid Attentive Unet for Segmentation of Lower Leg Muscles and Bones From MRI Scans For Musculoskeletal Research","authors":"Jiayi Zhu, Bart Bolsterlee, Brian V. Y. Chow, Yang Song, E. Meijering","doi":"10.1109/ISBI52829.2022.9761501","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761501","url":null,"abstract":"Musculoskeletal research such as studies of muscle growth in children with cerebral palsy (CP) often requires segmentation of muscles from magnetic resonance imaging (MRI) scans. This process has recently been automated by deep neural networks due to the costly and subjective nature of manual labelling. Deep neural networks typically perform well but, on average, tend to perform worse than human raters. Furthermore, deep neural networks need to generalize to scans of children with CP, which look different from scans of typically developing children because of differences in muscle size and composition, and typically constitute only a small portion of training data. To tackle those issues, we propose a novel end-to-end attention-based hybrid network that learns to segment musculoskeletal structures with a mixture of inter- and intra-slice features. The proposed network statistically significantly outperforms its contenders by a substantial margin and demonstrates robust generalization capabilities on scans of children with and without CP.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"40 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84477403","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}

引用次数: 2

Weakly Supervised Classification using Multi-Level Instance-Aware Optimization on Cervical Cytologic Image 基于多级实例感知优化的宫颈细胞学图像弱监督分类

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761702

Chenglu Zhu, Yuxuan Sun, Honglin Li, C. Cui, Shichuan Zhang, Jiatong Cai, Yang Ling

引用次数: 0

Towards Improved Robustness of Low-Dose CT Perfusion Imaging Via Joint Estimation of Structural CT and Functional CBF Images 通过结构CT和功能CBF图像联合估计提高低剂量CT灌注成像的鲁棒性

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761607

Viswanath P. Sudarshan, Vartika Sengar, Pavan Kumar Reddy, J. Gubbi, Arpan Pal

引用次数: 0

Combined Generation of Electrocardiogram and Cardiac Anatomy Models Using Multi-Modal Variational Autoencoders 利用多模态变分自编码器联合生成心电图和心脏解剖模型

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761590

M. Beetz, Abhirup Banerjee, Yuling Sang, V. Grau

{"title":"Combined Generation of Electrocardiogram and Cardiac Anatomy Models Using Multi-Modal Variational Autoencoders","authors":"M. Beetz, Abhirup Banerjee, Yuling Sang, V. Grau","doi":"10.1109/ISBI52829.2022.9761590","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761590","url":null,"abstract":"Understanding population-wide variability of the human heart is crucial to detect abnormalities and improve the assessment of both cardiac anatomy and function. While many computational modeling approaches have been developed to capture this variability separately for either cardiac anatomy or physiology, their complex interconnections have rarely been explored together. In this work, we propose a novel multi-modal variational autoencoder (VAE) capable of processing combined physiology and bitemporal anatomy information in the form of electrocardiograms (ECG) and 3D biventricular point clouds. Our method achieves high reconstruction accuracy on a UK Biobank dataset with Chamfer distances between predicted and input anatomies below the underlying image resolution and the ECG reconstructions outperforming a state-of-the-art benchmark approach specialized in ECG generation. We also evaluate its generative ability and find comparable populations of generated and gold standard anatomies, ECGs, and combined anatomy-ECG data in terms of common clinical metrics and maximum mean discrepancies.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81094872","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}

引用次数: 8