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An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution

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arXiv 2024年

作者： Duan, Minghong Qu, Linhao Yang, Zhiwei Wang, Manning Zhang, Chenxi 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 Academy for Engineering and Technology Fudan University Shanghai200433 China

High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinical work. Deep learning-based single-image superresolution (SISR) techniques provide an effective way to solve this problem. However, the existing SISR models applied in histopathology images can only work in fixed integer scaling factors, decreasing their applicability. Though methods based on implicit neural representation (INR) have shown promising results in arbitrary-scale super-resolution (SR) of natural images, applying them directly to histopathology images is inadequate because they have unique fine-grained image textures different from natural images. Thus, we propose an Implicit Self-Texture Enhancement-based dual-branch framework (ISTE) for arbitrary-scale SR of histopathology images to address this challenge. The proposed ISTE contains a feature aggregation branch and a texture learning branch. We employ the feature aggregation branch to enhance the learning of the local details for SR images while utilizing the texture learning branch to enhance the learning of high-frequency texture details. Then, we design a two-stage texture enhancement strategy to fuse the features from the two branches to obtain the SR images. Experiments on publicly available datasets, including TMA, HistoSR, and the TCGA lung cancer datasets, demonstrate that ISTE outperforms existing fixed-scale and arbitrary-scale SR algorithms across various scaling factors. Additionally, extensive experiments have shown that the histopathology images reconstructed by the proposed ISTE are applicable to downstream pathology image analysis tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： image texture

PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration

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arXiv 2022年

作者： Yuan, Mingzhi Li, Zhihao Jin, Qiuye Chen, Xinrong Wang, Manning Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai200032 China Academy for Engineering and Technology Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

Multi-instance point cloud registration is the problem of estimating multiple poses of source point cloud instances within a target point cloud. Solving this problem is challenging since inlier correspondences of one instance constitute outliers of all the other instances. Existing methods often rely on time-consuming hypothesis sampling or features leveraging spatial consistency, resulting in limited performance. In this paper, we propose PointCLM, a contrastive learning-based framework for mutli-instance point cloud registration. We first utilize contrastive learning to learn well-distributed deep representations for the input putative correspondences. Then based on these representations, we propose a outlier pruning strategy and a clustering strategy to efficiently remove outliers and assign the remaining correspondences to correct instances. Our method outperforms the state-of-the-art methods on both synthetic and real datasets by a large margin. The code will be made publicly available at http://***/phdymz/PointCLM. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

SP3 : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation

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arXiv 2024年

作者： Li, Shiman Zhao, Jiayue Liu, Shaolei Dai, Xiaokun Zhang, Chenxi Song, Zhijian The School of Basic Medical Science Fudan University 200433 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention 200032 China The Academy for Engineering and Technology Fudan University 200433 China

Deep learning-based medical image segmentation helps assist diagnosis and accelerate the treatment process while the model training usually requires large-scale dense annotation datasets. Weakly semi-supervised medical image segmentation is an essential application because it only requires a small amount of scribbles and a large number of unlabeled data to train the model, which greatly reduces the clinician’s effort to fully annotate images. To handle the inadequate supervisory information challenge in weakly semi-supervised segmentation (WSSS), a SuperPixel-Propagated Pseudo-label (SP3) learning method is proposed, using the structural information contained in superpixel for supplemental information. Specifically, the annotation of scribbles is propagated to superpixels and thus obtains a dense annotation for supervised training. Since the quality of pseudo-labels is limited by the low-quality annotation, the beneficial superpixels selected by dynamic thresholding are used to refine pseudo-labels. Furthermore, aiming to alleviate the negative impact of noise in pseudo-label, superpixel-level uncertainty is incorporated to guide the pseudo-label supervision for stable learning. Our method achieves state-of-the-art performance on both tumor and organ segmentation datasets under the WSSS setting, using only 3% of the annotation workload compared to fully supervised methods and attaining approximately 80% Dice score. Additionally, our method outperforms eight weakly and semi-supervised methods under both weakly supervised and semi-supervised settings. Results of extensive experiments validate the effectiveness and annotation efficiency of our weakly semi-supervised segmentation, which can assist clinicians in achieving automated segmentation for organs or tumors quickly and ultimately benefit patients. © 2024, CC BY.

关键词： Superpixels

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

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arXiv 2023年

作者： Yuan, Mingzhi Fu, Kexue Li, Zhihao Meng, Yucong Wang, Manning 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 Jinan China

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. Copyright © 2023, The Authors. All rights reserved.

关键词： Surface measurement

Pos-Bert: Point Cloud One-Stage Bert Pre-Training

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SSRN

SSRN 2023年

作者： Fu, Kexue Gao, Peng Liu, Shaolei Wang, Manning Digital Medical Research Center School of Basic Medical Science Fudan University China Shandong Computer Science Center China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China Shanghai AILab China

Recently, the pre-training paradigm combining Transformer and masked language modeling in BERT has achieved tremendous success not only in NLP, but also in images and point clouds. However, directly extending BERT from NLP to point clouds requires first training a discrete Variational AutoEncoder (dVAE) as the tokenizer, which results in a complex two-stage process, as in Point-BERT. Inspired by BERT and MoCo, we propose POS-BERT, a one-stage BERT pre-training method for point clouds. Specifically, we use the masked patch modeling (MPM) task to perform point cloud pre-training, which aims to recover masked patch information under the supervision of a tokenizer's output. Unlike Point-BERT, whose tokenizer is extra-trained and frozen, we propose a momentum tokenizer which is dynamically updated during training the Transformer. Furthermore, in order to better learn high-level semantic representation, we integrate contrastive learning into the proposed framework to maximize the class token consistency between augmented point cloud pairs. Experiments show that POS-BERT achieves the state-of-the-art performance on linear SVM classification of ModelNet40 with fixed feature extractors, and it exceeds Point-BERT by 3.5%. In addition, POS-BERT has significantly improved many downstream tasks, including fine-tuned classification, few-shot classification and part segmentation. The code and the trained model will be released soon. © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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

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

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

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

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Separate and Conquer: Decoupling Co-occurrence via Decomposi...

Conference on computer Vision and Pattern Recognition (CVPR)

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

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve segmentation tasks with-out dense annotations. However, 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 WSSS. In this work, we devise a ‘Separate and Conquer’ scheme SeCo to tackle this issue from di-mensions of image space and feature space. In the im-age space, we propose to ‘separate’ the co-occurring ob-jects 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 rep-resentation. 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, which guarantee the cor-rectness of knowledge and further facilitate the discrepancy among co-contexts. We streamline the multi-staged WSSS pipeline end-to-end and tackle this issue without external supervision. Extensive experiments are conducted, validating the efficiency of our method and the superiority over previous single-staged and even multi-staged competitors on PASCAL VOC and MS COCO. Code is available here.

关键词： Couplings computer vision Codes Semantic segmentation Semantics Pipelines Noise reduction

2d-3d Hierarchical Feature Fusion Network For Segmentation Of Bone Structure In Knee Mr image

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2d-3d Hierarchical Feature Fusion Network For Segmentation O...

IEEE International Symposium on Biomedical Imaging

作者： Hui Wang Demin Yao Jiayi Chen Yanjing Liu Wensheng Li Yonghong Shi Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Fudan University Shanghai China. Histology and Embryology School of Basic Medical Sciences Fudan University Shanghai China.

ISBN: (纸本)9781665412469;9781665429474

Automatic segmentation of knee bone structures is an important task in orthopedics diagnosis of knee disease based on MRI images. Inspired by doctors' diagnosis of knee in sagittal plane of MR image, we propose to first calculate the sagittal maximum intensity projection (MIP) of MR image, then construct a high precision 2D-3D hierarchical feature fusion network for automatic segmentation of knee based on convolutional encoding and decoding architecture. It includes: 1) A 2D bypass network extracting global features based on MIP; 2) A 3D backbone network calculating local details based on MR volume; 3) A feature fusion module integrating 2D global context and 3D local details hierarchically. Particularly, the global features as anchor points will be fused with the local details at each level of the encoding path to enrich the context of local details and improve the segmentation accuracy. Our method is verified on SKI10 dataset. The average dice coefficients of femur, femoral cartilage, tibia and tibia cartilage are 0.978, 0.848, 0.979 and 0.848, respectively, and the segmentation performance is far better than the state-of-the-art methods.

关键词： image segmentation Three-dimensional displays image coding Magnetic resonance imaging Semantics medical services Feature extraction

Towards Arbitrary-scale Histopathology image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement

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arXiv 2023年

作者： Qu, Linhao Duan, Minghong Yang, Zhiwei 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 Shanghai200032 China Academy for Engineering & Technology Fudan University Shanghai200433 China

Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance. Though implicit neural network-based methods have shown promising results in arbitrary-scale super-resolution of natural images, it is not effective to directly apply them in pathology images, because pathology images have special fine-grained image textures different from natural images. To address this challenge, we propose a dual-branch framework with an efficient self-texture enhancement mechanism for arbitrary-scale super-resolution of pathology images. Extensive experiments on two public datasets show that our method outperforms both existing fixed-scale and arbitrary-scale algorithms. To the best of our knowledge, this is the first work to achieve arbitrary-scale super-resolution in the field of pathology images. Codes will be available. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology