Information processing in medical imaging : proceedings of the ... conference最新文献_第7页

SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation 纵向医学图像生成的序列感知扩散模型

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2022-12-16 DOI: 10.1007/978-3-031-34048-2_30

Jee Seok Yoon, Chenghao Zhang, Heung-Il Suk, Jia Guo, Xiaoxia Li

引用次数: 5

Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation 用于标记高效组织病理学图像分割的人机交互组织原型学习

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2022-11-26 DOI: 10.48550/arXiv.2211.14491

W. Pan, Jiangpeng Yan, Hanbo Chen, Jiawei Yang, Zhe Xu, Xiu Li, Jianhua Yao

{"title":"Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation","authors":"W. Pan, Jiangpeng Yan, Hanbo Chen, Jiawei Yang, Zhe Xu, Xiu Li, Jianhua Yao","doi":"10.48550/arXiv.2211.14491","DOIUrl":"https://doi.org/10.48550/arXiv.2211.14491","url":null,"abstract":"Recently, deep neural networks have greatly advanced histopathology image segmentation but usually require abundant annotated data. However, due to the gigapixel scale of whole slide images and pathologists' heavy daily workload, obtaining pixel-level labels for supervised learning in clinical practice is often infeasible. Alternatively, weakly-supervised segmentation methods have been explored with less laborious image-level labels, but their performance is unsatisfactory due to the lack of dense supervision. Inspired by the recent success of self-supervised learning methods, we present a label-efficient tissue prototype dictionary building pipeline and propose to use the obtained prototypes to guide histopathology image segmentation. Particularly, taking advantage of self-supervised contrastive learning, an encoder is trained to project the unlabeled histopathology image patches into a discriminative embedding space where these patches are clustered to identify the tissue prototypes by efficient pathologists' visual examination. Then, the encoder is used to map the images into the embedding space and generate pixel-level pseudo tissue masks by querying the tissue prototype dictionary. Finally, the pseudo masks are used to train a segmentation network with dense supervision for better performance. Experiments on two public datasets demonstrate that our human-machine interactive tissue prototype learning method can achieve comparable segmentation performance as the fully-supervised baselines with less annotation burden and outperform other weakly-supervised methods. Codes will be available upon publication.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"89 1","pages":"679-691"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77622616","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

Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation 基于解剖感知对比蒸馏的自举半监督医学图像分割

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2022-06-06 DOI: 10.48550/arXiv.2206.02307

Chenyu You, Weichen Dai, L. Staib, J. Duncan

{"title":"Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation","authors":"Chenyu You, Weichen Dai, L. Staib, J. Duncan","doi":"10.48550/arXiv.2206.02307","DOIUrl":"https://doi.org/10.48550/arXiv.2206.02307","url":null,"abstract":"Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class distribution for both labeled and unlabeled medical images. However, medical image data in reality is commonly imbalanced (i.e., multi-class label imbalance), which naturally yields blurry contours and usually incorrectly labels rare objects. Moreover, it remains unclear whether all negative samples are equally negative. In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation. Specifically, we first develop an iterative contrastive distillation algorithm by softly labeling the negatives rather than binary supervision between positive and negative pairs. We also capture more semantically similar features from the randomly chosen negative set compared to the positives to enforce the diversity of the sampled data. Second, we raise a more important question: Can we really handle imbalanced samples to yield better performance? Hence, the key innovation in ACTION is to learn global semantic relationship across the entire dataset and local anatomical features among the neighbouring pixels with minimal additional memory footprint. During the training, we introduce anatomical contrast by actively sampling a sparse set of hard negative pixels, which can generate smoother segmentation boundaries and more accurate predictions. Extensive experiments across two benchmark datasets and different unlabeled settings show that ACTION significantly outperforms the current state-of-the-art semi-supervised methods.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 1","pages":"641-653"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42815533","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: 32

BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement 宾果:贝叶斯内在群体注册通过显式分层解纠缠

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2022-06-06 DOI: 10.1007/978-3-031-34048-2_25

Xin Wang, Xinzhe Luo, X. Zhuang

引用次数: 0

DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation DTU-Net:曲线结构分割的拓扑相似性学习

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2022-05-23 DOI: 10.48550/arXiv.2205.11115

Manxi Lin, Zahra Bashir, M. Tolsgaard, A. Christensen, Aasa Feragen

{"title":"DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation","authors":"Manxi Lin, Zahra Bashir, M. Tolsgaard, A. Christensen, Aasa Feragen","doi":"10.48550/arXiv.2205.11115","DOIUrl":"https://doi.org/10.48550/arXiv.2205.11115","url":null,"abstract":"Curvilinear structure segmentation is important in medical imaging, quantifying structures such as vessels, airways, neurons, or organ boundaries in 2D slices. Segmentation via pixel-wise classification often fails to capture the small and low-contrast curvilinear structures. Prior topological information is typically used to address this problem, often at an expensive computational cost, and sometimes requiring prior knowledge of the expected topology. We present DTU-Net, a data-driven approach to topology-preserving curvilinear structure segmentation. DTU-Net consists of two sequential, lightweight U-Nets, dedicated to texture and topology, respectively. While the texture net makes a coarse prediction using image texture information, the topology net learns topological information from the coarse prediction by employing a triplet loss trained to recognize false and missed splits in the structure. We conduct experiments on a challenging multi-class ultrasound scan segmentation dataset as well as a well-known retinal imaging dataset. Results show that our model outperforms existing approaches in both pixel-wise segmentation accuracy and topological continuity, with no need for prior topological knowledge.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"318 1","pages":"654-666"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76439568","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

Blob Loss: Instance Imbalance Aware Loss Functions for Semantic Segmentation Blob损失:语义分割的实例不平衡感知损失函数

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2022-05-17 DOI: 10.48550/arXiv.2205.08209

F. Kofler, Suprosanna Shit, I. Ezhov, L. Fidon, Rami Al-Maskari, Hongwei Li, H. Bhatia, T. Loehr, M. Piraud, Ali Erturk, J. Kirschke, J. Peeken, Tom Kamiel Magda Vercauteren, C. Zimmer, B. Wiestler, Bjoern H Menze

{"title":"Blob Loss: Instance Imbalance Aware Loss Functions for Semantic Segmentation","authors":"F. Kofler, Suprosanna Shit, I. Ezhov, L. Fidon, Rami Al-Maskari, Hongwei Li, H. Bhatia, T. Loehr, M. Piraud, Ali Erturk, J. Kirschke, J. Peeken, Tom Kamiel Magda Vercauteren, C. Zimmer, B. Wiestler, Bjoern H Menze","doi":"10.48550/arXiv.2205.08209","DOIUrl":"https://doi.org/10.48550/arXiv.2205.08209","url":null,"abstract":"Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Dice coefficient (DSC). By design, DSC can tackle class imbalance, however, it does not recognize instance imbalance within a class. As a result, a large foreground instance can dominate minor instances and still produce a satisfactory DSC. Nevertheless, detecting tiny instances is crucial for many applications, such as disease monitoring. For example, it is imperative to locate and surveil small-scale lesions in the follow-up of multiple sclerosis patients. We propose a novel family of loss functions, emph{blob loss}, primarily aimed at maximizing instance-level detection metrics, such as F1 score and sensitivity. emph{Blob loss} is designed for semantic segmentation problems where detecting multiple instances matters. We extensively evaluate a DSC-based emph{blob loss} in five complex 3D semantic segmentation tasks featuring pronounced instance heterogeneity in terms of texture and morphology. Compared to soft Dice loss, we achieve 5% improvement for MS lesions, 3% improvement for liver tumor, and an average 2% improvement for microscopy segmentation tasks considering F1 score.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"85 1","pages":"755-767"},"PeriodicalIF":0.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83889020","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

Modeling the Shape of the Brain Connectome via Deep Neural Networks 通过深度神经网络建模大脑连接组的形状

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2022-03-06 DOI: 10.1007/978-3-031-34048-2_23

Haocheng Dai, M. Bauer, P. Fletcher, S. Joshi

引用次数: 0

A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework 一种新的双向无监督域自适应分割框架

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-08-18 DOI: 10.1007/978-3-030-78191-0_38

Munan Ning, Cheng Bian, Dong Wei, Shuang Yu, Chenglang Yuan, Yaohua Wang, Yang Guo, Kai Ma, Yefeng Zheng

引用次数: 13

Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography 分割与多个可接受的注释:心肌分割对比超声心动图的一个案例研究

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-06-29 DOI: 10.1007/978-3-030-78191-0_37

Dewen Zeng, Mingqi Li, Yukun Ding, Xiaowei Xu, Qiu Xie, Ruixue Xu, Hongwen Fei, Meiping Huang, Zhuang Jian, Yiyu Shi

引用次数: 8

A Higher Order Manifold-Valued Convolutional Neural Network with Applications to Diffusion MRI Processing 高阶流形值卷积神经网络在弥散核磁共振处理中的应用

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-06-28 DOI: 10.1007/978-3-030-78191-0_24

Jose J. Bouza, Chun-Hao Yang, D. Vaillancourt, B. Vemuri

引用次数: 4