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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

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Research of an Electromagnetic Tracking Method Based on Two Electrically-controlled Magnetic Sources

Research of an Electromagnetic Tracking Method Based on Two ...

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2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： Min Sha Yifeng Wang Ning Ding Xiaomei Wu Zuxiang Fang Department of Electronic Engineering Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

ISBN: (纸本)9781479919819

This paper presents an electromagnetic tracking method based on two electrically-controlled magnetic *** source consists of three mutual orthogonal *** method can skillfully determine the rotation angles of the maximum magnetic flux density vector(MMFD V) generated by a magnetic source according to the Biot-Savart *** the position of the sensor can be calculated with a noniterative geometrical algorithm in real *** results demonstrate a good performance of this method.

关键词： electromagnetic tracking MMFDV fast determination algorithm finite element analysis

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The rise of AI language pathologists: exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification 23

The rise of AI language pathologists: exploring two-level pr...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Linhao Qu Xiaoyuan Luo Kexue Fu Manning Wang Zhijian Song Digital Medical Research Center School of Basic Medical Science Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention

This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4. Since a WSI is too large and needs to be divided into patches for processing, WSI classification is commonly approached as a Multiple Instance Learning (MIL) problem. In this context, each WSI is considered a bag, and the obtained patches are treated as instances. The objective of FSWC is to classify both bags and instances with only a limited number of labeled bags. Unlike conventional few-shot learning problems, FSWC poses additional challenges due to its weak bag labels within the MIL framework. Drawing inspiration from the recent achievements of vision-language models (V-L models) in downstream few-shot classification tasks, we propose a two-level prompt learning MIL framework tailored for pathology, incorporating language prior knowledge. Specifically, we leverage CLIP to extract instance features for each patch, and introduce a prompt-guided pooling strategy to aggregate these instance features into a bag feature. Subsequently, we employ a small number of labeled bags to facilitate few-shot prompt learning based on the bag features. Our approach incorporates the utilization of GPT-4 in a question-and-answer mode to obtain language prior knowledge at both the instance and bag levels, which are then integrated into the instance and bag level language prompts. Additionally, a learnable component of the language prompts is trained using the available few-shot labeled data. We conduct extensive experiments on three real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer, demonstrating the notable performance of the proposed method in bag and instance classification. Codes will be available at https://***/miccaiif/TOP.

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Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification

Boosting Whole Slide Image Classification from the Perspecti...

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International Conference on computer Vision (ICCV)

作者： Linhao Qu Zhiwei Yang Minghong Duan Yingfan Ma Shuo Wang Manning Wang Zhijian Song Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention

Bag-based multiple instance learning (MIL) methods have become the mainstream for Whole Slide Image (WSI) classification. However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications. In this paper, we propose MILBooster, a powerful dual-scale multi-stage MIL framework to address these issues from the perspectives of distribution, correlation, and magnification. Specifically, to address issue (1), we propose a plug-and-play bag filter that effectively increases the positive instance ratio of positive bags. For issue (2), we propose a novel window-based Transformer architecture called PiceBlock to model the correlation between local and global features of pathology images. For issue (3), we propose a dual-branch architecture to process different magnifications and design an information interaction module called Scale Mixer for efficient information interaction between them. We conducted extensive experiments on four clinical WSI classification tasks using three datasets. MILBooster achieved new state-of-the-art performance on all these tasks. Codes will be available at https://***/miccaiif/MILBooster.

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A Learnable self-supervised task for unsupervised domain adaptation on point clouds

arXiv

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arXiv 2021年

作者： Luo, Xiaoyuan Liu, Shaolei Fu, Kexue Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention

Deep neural networks have achieved promising performance in supervised point cloud applications, but manual annotation is extremely expensive and time-consuming in supervised learning schemes. Unsupervised domain adaptation (UDA) addresses this problem by training a model with only labeled data in the source domain but making the model generalize well in the target domain. Existing studies show that self-supervised learning using both source and target domain data can help improve the adaptability of trained models, but they all rely on hand-crafted designs of the self-supervised tasks. In this paper, we propose a learnable self-supervised task and integrate it into a self-supervision-based point cloud UDA architecture. Specifically, we propose a learnable nonlinear transformation that transforms a part of a point cloud to generate abundant and complicated point clouds while retaining the original semantic information, and the proposed self-supervised task is to reconstruct the original point cloud from the transformed ones. In the UDA architecture, an encoder is shared between the networks for the self-supervised task and the main task of point cloud classification or segmentation, so that the encoder can be trained to extract features suitable for both the source and the target domain data. Experiments on PointDA-10 and PointSegDA datasets show that the proposed method achieves new state-of-the-art performance on both classification and segmentation tasks of point cloud UDA. Code will be made publicly available. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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A novel method with a deep network and directional edges for automatic detection of a fetal head

A novel method with a deep network and directional edges for...

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European Signal Processing Conference (EUSIPCO)

作者： Siqing Nie Jinhua Yu Ping Chen Jianqiu Zhang Yuanyuan Wang Department of Electronic Engineering Fudan University Shanghai China Key laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Ultrasound Department Tongji University Shanghai China

In this paper, we propose a novel method for the automatic detection of fetal head in 2D ultrasound images. Fetal head detection has been a challenging task, as the ultrasound images usually have poor quality, the structures contained in the images are complex, and the gray scale distribution is highly variable. Our approach is based on a deep belief network and a modified circle detection method. The whole process can be divided into two steps: first, a deep learning architecture is applied to search the whole image and determine the result patch that contains the entire fetal head; second, a modified circle detection method is used along with Hough transform to detect the position and size of the fetal head. In order to validate our method, experiments are performed on both synthetic data and clinic ultrasound data. A good performance of the proposed method is shown in the paper.

关键词： Head Image edge detection Ultrasonic imaging Magnetic heads Training Europe Signal processing

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

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

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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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3D Clothed Human Reconstruction From One In-the-Wild RGB Image

3D Clothed Human Reconstruction From One In-the-Wild RGB Ima...

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IEEE International Conference on Image Processing

作者： Liangjing Shao Benshuang Chen Xinrong Chen Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

In recent years, much achievement have been made in the field of 3D clothed human reconstruction. However, most of researches performed not well for reconstruction from in-the-wild images due to the domain gap between the synthetic images of training datasets and the in-the-wild images. In this study, a modular model, including clothes encoder, body encoder and cloth generator, is proposed to perform 3D clothed human reconstruction from one single-view in-the-wild RGB image. In particular, we introduce the adaptive aggregation of convolution and multi-head attention into the cloth encoder and apply the adjustment of the segmentation at the preprocessing stage. According to experiments on MSCOCO and 3DPW datasets, the proposed method achieves state-of-the-art performance on 3D clothed human reconstruction from in-the-wild images compared with previous works.

关键词： Training Solid modeling Image segmentation Adaptation models Three-dimensional displays Convolution Data preprocessing

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A Fast Global Maximum Searching Strategy in Electromagnetic Tracking

A Fast Global Maximum Searching Strategy in Electromagnetic ...

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2015 World Conference on Control, Electronics and Electrical Engineering(WCEE 2015)

作者： Yifeng Wang Xiaomei Wu Jianguo Yu Department of Electronic Engineering Fudan University Digital Medical Research Center of Fudan University Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI)

In a rotating coil based magnetic tracking system,so long as the rotating coil has seized the direction of the magnetic sensor,geometric algorithm can compute the position and orientation of the target object without tedious iterative ***,the rotation process of the coil is still time *** the coil will be rotated in a plane,the measured magnetic field density should change as a sine/cosine-like *** on this,we proposes a searching strategy in which field densities are measured along four sparse even spaced directions to lock an initial searching range,and then the searching range is continuously halved around the measured maximum field density until a required tracking precision is *** results show that the geometric tracking algorithm with this global searching strategy can track the target object with higher efficiency and higher precision than the algorithm without this searching ***,in the condition of low field noises,the optimized non-iterative algorithm with global searching strategy has better precision than the traditional iterative algorithms as well.

关键词： adaptive algorithm,binary search,global optimization,local optimization,magnetic tracking,non-iterative algorithm.

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Rethinking Multi-Exposure Image Fusion with Extreme and Diverse Exposure Levels: A Robust Framework Based on Fourier Transform and Contrastive Learning

SSRN

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SSRN 2022年

作者： Qu, Linhao Liu, Shaolei Wang, Manning Song, Zhijian Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China

Multi-exposure image fusion (MEF) is an important technique for generating high dynamic range images. However, most existing MEF studies focus on fusing a moderately over-exposed image and a moderately under-exposed image, and they are not robust in fusing images with extreme and diverse exposure levels. In this paper, we propose a robust MEF framework based on Fourier transform and contrastive learning. Specifically, we develop a Fourier transform-based pixel intensity transfer strategy to synthesize images with diverse exposure levels from normally exposed natural images and train an encoder-decoder network to reconstruct the original natural image. In this way, the encoder and decoder can learn to extract features from images with diverse exposure levels and generate fused images with normal exposure. We propose a contrastive regularization loss to further enhance the capability of the network in recovering normal exposure levels. In addition, we construct an extreme MEF benchmark dataset and a random MEF benchmark dataset for a more comprehensive evaluation of MEF algorithms. We extensively compare our method with nine competitive traditional and deep learning-based MEF algorithms on three benchmark datasets, and our method outperforms the other methods in both subjective visual effects and objective evaluation metrics. Our code, datasets and all fused images will be released. © 2022, The Authors. All rights reserved.

关键词： Fourier transforms

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