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Organ at risk segmentation in head and neck CT images by using a two-stage segmentation framework based on 3D U-Net

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arXiv 2018年

作者： Wang, Yueyue Zhao, Liang Song, Zhijian Wang, Manning School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Digital Medical Research Center Fudan University Shanghai200032 China

Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer. This segmentation task is challenging for both human and automatic algorithms because of the relatively large number of OARs to be segmented, the large variability of the size and morphology across different OARs, and the low contrast of between some OARs and the background. In this paper, we proposed a two-stage segmentation framework based on 3D U-Net. In this framework, the segmentation of each OAR is decomposed into two sub-tasks: locating a bounding box of the OAR and segment it from a small volume within the bounding box, and each sub-tasks is fulfilled by a dedicated 3D U-Net. The decomposition makes each of the two sub-tasks much easier, so that they can be better completed. We evaluated the proposed method and compared it to state-of-the-art methods by using the MICCAI 2015 Challenge dataset. In terms of the boundary-based metric 95HD, the proposed method ranked first in eight of all nine OARs and ranked second in the other OAR. In terms of the area-based metric DSC, the proposed method ranked first in six of the nine OARs and ranked second in the other three OARs with small difference with the first one. Copyright © 2018, The Authors. All rights reserved.

关键词： computerized tomography

OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology image Classification

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arXiv 2023年

作者： Qu, Linhao Ma, Yingfan Yang, Zhiwei Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Academy for Engineering & Technology Fudan University Shanghai200433 China

Active learning (AL) is an effective approach to select the most informative samples to label so as to reduce the annotation cost. Existing AL methods typically work under the closed-set assumption, i.e., all classes existing in the unlabeled sample pool need to be classified by the target model. However, in some practical clinical tasks, the unlabeled pool may contain not only the target classes that need to be fine-grainedly classified, but also non-target classes that are irrelevant to the clinical tasks. Existing AL methods cannot work well in this scenario because they tend to select a large number of non-target samples. In this paper, we formulate this scenario as an open-set AL problem and propose an efficient framework, OpenAL, to address the challenge of querying samples from an unlabeled pool with both target class and non-target class samples. Experiments on fine-grained classification of pathology images show that OpenAL can significantly improve the query quality of target class samples and achieve higher performance than current state-of-the-art AL methods. Code is available at https://***/miccaiif/OpenAL. Copyright © 2023, The Authors. All rights reserved.

关键词： image classification

Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation

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arXiv 2024年

作者： Yang, Zhiwei Meng, Yucong Fu, Kexue Wang, Shuo Song, Zhijian Academy for Engineering & Technology Fudan University Shanghai200433 China Digital Medical Research Center School of Basic Medical Science Fudan University China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Shandong Computer Science Center China

Weakly supervised semantic segmentation (WSSS) with image-level labels intends to achieve dense tasks without laborious annotations. However, due to the ambiguous contexts and fuzzy regions, the performance of WSSS, especially the stages of generating Class Activation Maps (CAMs) and refining pseudo masks, widely suffers from ambiguity while being barely noticed by previous literature. In this work, we propose UniA, a unified single-staged WSSS framework, to efficiently tackle this issue from the perspective of uncertainty inference and affinity diversification, respectively. When activating class objects, we argue that the false activation stems from the bias to the ambiguous regions during the feature extraction. Therefore, we design a more robust feature representation with a probabilistic Gaussian distribution and introduce the uncertainty estimation to avoid the bias. A distribution loss is particularly proposed to supervise the process, which effectively captures the ambiguity and models the complex dependencies among features. When refining pseudo labels, we observe that the affinity from the prevailing refinement methods intends to be similar among ambiguities. To this end, an affinity diversification module is proposed to promote diversity among semantics. A mutual complementing refinement is proposed to initially rectify the ambiguous affinity with multiple inferred pseudo labels. More importantly, a contrastive affinity loss is further designed to diversify the relations among unrelated semantics, which reliably propagates the diversity into the whole feature representations and helps generate better pseudo masks. Extensive experiments are conducted on PASCAL VOC, MS COCO, and medical ACDC datasets, which validate the efficiency of UniA tackling ambiguity and the superiority over recent single-staged or even most multi-staged competitors. Codes will be publicly available here. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation

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arXiv 2024年

作者： Yang, Zhiwei Fu, Kexue Duan, Minghong Qu, Linhao Wang, Shuo Song, Zhijian Academy for Engineering and Technology Fudan University China Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China Shandong Computer Science Center National Supercomputer Center in Jinan China

Attributed to the frequent coupling of co-occurring objects and the limited supervision from image-level labels, the challenging co-occurrence problem is widely present and leads to false activation of objects in weakly supervised semantic segmentation (WSSS). In this work, we devise a’Separate and Conquer’ scheme SeCo to tackle this issue from dimensions of image space and feature space. In the image space, we propose to’separate’ the co-occurring objects with image decomposition by subdividing images into patches. Importantly, we assign each patch a category tag from Class Activation Maps (CAMs), which spatially helps remove the co-context bias and guide the subsequent representation. In the feature space, we propose to’conquer’ the false activation by enhancing semantic representation with multi-granularity knowledge contrast. To this end, a dual-teacher-single-student architecture is designed and tag-guided contrast is conducted to guarantee the correctness of knowledge and further facilitate the discrepancy among co-occurring objects. We streamline the multi-staged WSSS pipeline end-to-end and tackle co-occurrence without external supervision. Extensive experiments are conducted, validating the efficiency of our method tackling co-occurrence and the superiority over previous single-staged and even multi-staged competitors on PASCAL VOC and MS COCO. Code will be available here. Copyright © 2024, The Authors. All rights reserved.

关键词： Chemical activation

Rethinking Multiple Instance Learning for Whole Slide image Classification: A Good Instance Classifier is All You Need

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arXiv 2023年

作者： Qu, Linhao Ma, Yingfan Luo, Xiaoyuan Guo, Qinhao Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Department of Gynecologic Oncology Fudan University Shanghai Cancer Center 270 Dong-An Road Shanghai200032 China

Weakly supervised whole slide image classification is usually formulated as a multiple instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut out of it are treated as instances. Existing methods either train an instance classifier through pseudo-labeling or aggregate instance features into a bag feature through attention mechanisms and then train a bag classifier, where the attention scores can be used for instance-level classification. However, the pseudo instance labels constructed by the former usually contain a lot of noise, and the attention scores constructed by the latter are not accurate enough, both of which affect their performance. In this paper, we propose an instance-level MIL framework based on contrastive learning and prototype learning to effectively accomplish both instance classification and bag classification tasks. To this end, we propose an instance-level weakly supervised contrastive learning algorithm for the first time under the MIL setting to effectively learn instance feature representation. We also propose an accurate pseudo label generation method through prototype learning. We then develop a joint training strategy for weakly supervised contrastive learning, prototype learning, and instance classifier training. Extensive experiments and visualizations on four datasets demonstrate the powerful performance of our method. Codes are available at https://***/miccaiif/INS. Copyright © 2023, The Authors. All rights reserved.

关键词： image classification

MoRe: Class Patch Attention Needs Regularization for Weakly Supervised Semantic Segmentation

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arXiv 2024年

作者： Yang, Zhiwei Meng, Yucong Fu, Kexue Wang, Shuo Song, Zhijian Academy for Engineering and Technology Fudan University Shanghai200433 China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai200032 China China

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically uses Class Activation Maps (CAM) to achieve dense predictions. Recently, Vision Transformer (ViT) has provided an alternative to generate localization maps from class-patch attention. However, due to insufficient constraints on modeling such attention, we observe that the Localization Attention Maps (LAM) often struggle with the artifact issue, i.e., patch regions with minimal semantic relevance are falsely activated by class tokens. In this work, we propose MoRe to address this issue and further explore the potential of LAM. Our findings suggest that imposing additional regularization on class-patch attention is necessary. To this end, we first view the attention as a novel directed graph and propose the Graph Category Representation module to implicitly regularize the interaction among class-patch entities. It ensures that class tokens dynamically condense the related patch information and suppress unrelated artifacts at a graph level. Second, motivated by the observation that CAM from classification weights maintains smooth localization of objects, we devise the Localization-informed Regularization module to explicitly regularize the class-patch attention. It directly mines the token relations from CAM and further supervises the consistency between class and patch tokens in a learnable manner. Extensive experiments are conducted on PASCAL VOC and MS COCO, validating that MoRe effectively addresses the artifact issue and achieves state-of-the-art performance, surpassing recent single-stage and even multi-stage methods. Code is available at https://***/zwyang6/MoRe. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

key-Point Based Automated Diagnosis for Alveolar Dehiscence in Mandibular Incisors Using Convolutional Neural Network

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SSRN

SSRN 2022年

作者： Liu, Tianyu Ye, Yingzhi Liu, Chengcheng Chen, Jing Liu, Yuehua Xing, Wenyu Ta, Dean Academy for Engineering and Technology Fudan University Shanghai200433 China Department of Orthodontics Shanghai Stomatological Hospital School of Stomatology Fudan University Shanghai200001 China Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases Fudan University Shanghai200001 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Center for Medical Engineering School of Information Science and Technology Fudan University Shanghai200438 China

The aim of this study was to propose an automated diagnosis method for alveolar dehiscence in the anterior teeth using convolutional neural network (CNN).The Cone-beam computed tomography (CBCT) scanning was performed on 387 orthodontic patients at shanghai Stomatological Hospital. A total of 1017 mandibular incisors with the largest labiolingual sectional images were obtained from the CBCT data. We proposed two strategies of automated diagnosis methods for alveolar dehiscence. The first strategy (the binary classification method (BCM)) was to take the task directly as a classic binary classification. The second strategy (the key-point based method (KPM)) was to use the CNN to search two key points (i.e., the cement-enamel junction (CEJ) and the alveolar crest (AC)) firstly and then make a diagnosis according to the distance between the two key points. In both CNN strategies, 90% of the mandibular incisor images were assigned to the training dataset, and the rest 10% were assigned to the testing *** BCM showed limited performance in the diagnosis of alveolar dehiscence, with diagnostic accuracy below 70% for all the five classification networks. The KPM showed superior diagnostic performance with an accuracy of 90.9%, a sensitivity of 81.4% and a specificity of 94.4%, respectively, in the testing *** results proved that the diagnosis of alveolar dehiscence could be realized using CNN and the proposed KPM have the advantage of high accuracy and real-time performance. This study suggests that the KPM might have the potential for the diagnosis of alveolar dehiscence in the clinic. © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

Histological grade and type classification of glioma using Magnetic Resonance Imaging

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Histological grade and type classification of glioma using M...

International Congress on image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI)

作者： Yuan Gao Zhifeng Shi Yuanyuan Wang Jinhua Yu Liang Chen Yi Guo Qi Zhang Ying Mao Department of Electronic Engineering Fudan University Shanghai China Department of Neurosurgery Fudan University Shanghai China Key laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China School of Communication and Information Engineering Shanghai University Shanghai China

ISBN: (纸本)9781509037117

Glioma is one of the most common brain tumors with high mortality and its histological grading and typing is important both in therapeutic decision and prognosis evaluation. This paper aims at using the high-throughput image feature analysis method to estimate the histological grade and type of a patient by using Magnetic Resonance Imaging (MRI) instead of histological examination. The proposed method consists of the initial label definition, the region-of-interest delineation, the self-adaptive feature extraction, the feature subset selection, and the multi-class voting classification. Hereinto, a novel feature extraction strategy is designed, which could avoid the MRI scan diversity so as to get the robust feature extraction result and make the proposed framework more stable and effective. This method was validated on a database of 124 patients with the grade II to IV of 78, 25, and 21, and with astrocytoma, oligodendroglioma, oligoastrocytoma of 86, 16, and 22, respectively. We show that by using the leave-one-out cross-validation, the multi-class classification accuracy and macro average could reach 88.71%, 0.8362 respectively for the grade classification, and 70.97%, 0.5692 respectively for the type classification. It can be concluded that the histological grade and subtype information could be estimated from the MRI image analysis.

关键词： Feature extraction Magnetic resonance imaging Tumors Support vector machines Optimization Hospitals image segmentation

Residual block-based multi-label classification and localization network with integral regression for vertebrae labeling

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arXiv 2020年

作者： Qin, Chunli Yao, Demin Zhuang, Han Wang, Hui Shi, Yonghong Song, Zhijian Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai Key Laboratory of Medical Imaging Computing Computer Assisted Intervention Shanghai200032 China Medical Research Center and Department of Anatomy Histology and Embryology School of Basic Medical Sciences Fudan University

Accurate identification and localization of the vertebrae in CT scans is a critical and standard preprocessing step for clinical spinal diagnosis and treatment. Existing methods are mainly based on the integration of multiple neural networks, and most of them use the Gaussian heat map to locate the vertebrae's centroid. However, the process of obtaining the vertebrae's centroid coordinates using heat maps is non-differentiable, so it is impossible to train the network to label the vertebrae ***, for end-to-end differential training of vertebra coordinates on CT scans, a robust and accurate automatic vertebral labeling algorithm is proposed in this study. Firstly, a novel residual-based multi-label classification and localization network is developed, which can capture multi-scale features, but also utilize the residual module and skip connection to fuse the multi-level features. Secondly, to solve the problem that the process of finding coordinates is non-differentiable and the spatial structure is not destructible, integral regression module is used in the localization network. It combines the advantages of heat map representation and direct regression coordinates to achieve end-to-end training, and can be compatible with any key point detection methods of medical image based on heat map. Finally, multi-label classification of vertebrae is carried out, which use bidirectional long short term memory (Bi-LSTM) to enhance the learning of long contextual information to improve the classification performance. The proposed method is evaluated on a challenging dataset and the results are significantly better than the state-of-the-art methods (mean localization error Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)