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Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

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arXiv 2023年

作者： Li, Shiman Wang, Haoran Meng, Yucong Zhang, Chenxi 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

Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning using unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemma in multi-organ segmentation. We first review the traditional fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends. Copyright © 2023, The Authors. All rights reserved.

关键词： computer aided diagnosis

An Attention-Based Signed Distance Field Estimation Method for Hand-Object Reconstruction

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An Attention-Based Signed Distance Field Estimation Method f...

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Xinkang Zhang Xinhan Di Xiaokun Dai Xinrong Chen Academy for engineering & technology Fudan University shanghai China Bloo company shanghai China Academy for engineering & technology Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Fudan University shanghai China

Joint reconstruction of hands and objects from monocular RGB images is a challenging task. In this work, we present a novel hybrid model for joint reconstruction of hands and objects. The model proposed consists of three key modules. Among them, multi-scale attention feature extractor is designed to enhance cross-scale in-formation extraction. Attention-based graph encoder can encode the graph information of the hands. Interacting attention module is applied to fuse information between hands and object. Test re-sults on ObMan dataset [6] show that our method outperforms the state-of-the-art method around 13.97% and 15.75% in $\mathrm{H}_{\text{se}}$ and $\mathrm{H}_{\text{je}}$ respectively.

关键词： Solid modeling Three-dimensional displays Fuses Estimation Virtual reality User interfaces Reconstruction algorithms

Exploring CLIP’s Dense Knowledge for Weakly Supervised Semantic Segmentation

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arXiv 2025年

作者： Yang, Zhiwei Meng, Yucong Fu, Kexue Tang, Feilong Wang, Shuo Song, Zhijian Academy for Engineering and Technology Fudan University Shanghai200433 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shandong Computer Science Center National Supercomputer Center in Jinan China

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Activation Maps (CAMs). Recently, Contrastive Language-image Pre-training (CLIP) has been introduced in WSSS. However, recent methods primarily focus on image-text alignment for CAM generation, while CLIP’s potential in patch-text alignment remains unexplored. In this work, we propose ExCEL to explore CLIP’s dense knowledge via a novel patch-text alignment paradigm for WSSS. Specifically, we propose Text Semantic Enrichment (TSE) and Visual Calibration (VC) modules to improve the dense alignment across both text and vision modalities. To make text embeddings semantically informative, our TSE module applies Large Language Models (LLMs) to build a dataset-wide knowledge base and enriches the text representations with an implicit attribute-hunting process. To mine fine-grained knowledge from visual features, our VC module first proposes Static Visual Calibration (SVC) to propagate fine-grained knowledge in a non-parametric manner. Then Learnable Visual Calibration (LVC) is further proposed to dynamically shift the frozen features towards distributions with diverse semantics. With these enhancements, ExCEL not only retains CLIP’s training-free advantages but also significantly outperforms other state-of-the-art methods with much less training cost on PASCAL VOC and MS COCO. Code is available at https://***/zwyang6/ExCEL. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

A comprehensive survey on deep active learning in medical image analysis

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arXiv 2023年

作者： Wang, Haoran Jin, Qiuye Li, Shiman Liu, Siyu Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Thuwal23955 Saudi Arabia

Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first time, we provide a detailed summary of the integration of active learning with other label-efficient techniques, such as semi-supervised, self-supervised learning, and so on. We also summarize active learning works that are specifically tailored to medical image analysis. Additionally, we conduct a thorough comparative analysis of the performance of different AL methods in medical image analysis with experiments. In the end, we offer our perspectives on the future trends and challenges of active learning and its applications in medical image analysis. © 2023, CC BY.

关键词： Deep learning

Boosting 3D Point Cloud Registration by Transferring Multi-modality Knowledge

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arXiv 2023年

作者： Yuan, Mingzhi Huang, Xiaoshui Fu, Kexue Li, Zhihao Wang, Manning The Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China The Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China The Shanghai artificial intelligence Lab China

关键词： Convolution

PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration

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PointMBF: A Multi-scale Bidirectional Fusion Network for Uns...

International Conference on computer Vision (ICCV)

作者： Mingzhi Yuan Kexue Fu Zhihao Li Yucong Meng Manning Wang 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)

Point cloud registration is a task to estimate the rigid transformation between two unaligned scans, which plays an important role in many computer vision applications. Previous learning-based works commonly focus on supervised registration, which have limitations in practice. Recently, with the advance of inexpensive RGB-D sensors, several learning-based works utilize RGB-D data to achieve unsupervised registration. However, most of existing unsupervised methods follow a cascaded design or fuse RGB-D data in a unidirectional manner, which do not fully exploit the complementary information in the RGB-D data. To leverage the complementary information more effectively, we propose a network implementing multi-scale bidirectional fusion between RGB images and point clouds generated from depth images. By bidirectionally fusing visual and geometric features in multi-scales, more distinctive deep features for correspondence estimation can be obtained, making our registration more accurate. Extensive experiments on ScanNet and 3DMatch demonstrate that our method achieves new state-of-the-art performance. Code will be released at https://***/phdymz/PointMBF.

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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

Towards More Precise Automatic Analysis: A Comprehensive Survey of Deep Learning-based Multi-organ Segmentation

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arXiv 2023年

作者： Liu, Xiaoyu Qu, Linhao Xie, Ziyue Zhao, Jiayue Shi, Yonghong Song, Zhijian Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai200032 China

Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation method has far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. For the first time, from the perspective of full and imperfect annotation, we comprehensively compile 161 studies on deep learning-based multi-organ segmentation in multiple regions such as the head and neck, chest, and abdomen, containing a total of 214 related references. The method based on full annotation summarizes the existing methods from four aspects: network architecture, network dimension, network dedicated modules, and network loss function. The method based on imperfect annotation summarizes the existing methods from two aspects: weak annotation-based methods and semi annotation-based methods. We also summarize frequently used datasets for multi-organ segmentation and discuss new challenges and new research trends in this field. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

Diffusion Model with Self-supervised Training for Subject-aware Photoplethysmography Recovery

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Diffusion Model with Self-supervised Training for Subject-aw...

IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Daomiao Wang Qihan Hu Xujian Feng Cuiwei Yang Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

Photoplethysmography (PPG) has emerged as a promising technology for wearable personal healthcare due to its non-invasive and cost-effective nature. However, owing to the vulnerability to various noise, the useful PPG signals are few and the usability of PPG-based systems are limited. In this paper, we address the challenge of recovering extremely corrupted PPG waveforms with severe distortions, which lead to missing data. Specifically, we propose a novel diffusion-based framework that incorporates remaining values and subject-aware embedding, along with a self-supervised generative training strategy. Extensive experiments were performed on two datasets under three missing rates and two missing patterns. Our proposed model achieves significant improvements of nearly 100% over existing imputation models on quantitative metrics, and together with the visualization, demonstrating its effectiveness for data distribution generation and corrupted PPG restoration. Our proposed method also provides a new perspective for PPG denoising.

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