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Clinical Cancer Bulletin 2022年第3期1卷 167-171页

作者： Ye GU Kai XUE Quan LIU Huankang ZHANG Hongmeng YU Department of Neurosurgery Eye&ENT HospitalFudan UniversityShanghaiChina Department of Otolaryngology Eye&ENT HospitalFudan UniversityShanghaiChina Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Fudan UniversityShanghaiChina Research Units of New Technologies of Endoscopic Surgery in Skull Base Tumor Chinese Academy of Medical SciencesShanghaiChina

Objective Although intracranial and extracranial communicating meningiomas are uncommon in clinical settings,they pose a considerable challenge for ***,one-stage surgery should be more comprehensively explained as it is not frequently reported as a first-line *** report A 27-year-old man with a massive intracranial and extracranial communicating meningioma was admitted with nasal congestion and mild numbness on the left side of his face.A combined transcranial and endoscopic endonasal approach was performed in a single day aided by preoperative tumor *** tumor was nearly completely removed without any serious ***-up revealed the disappearance of diplopia,with the patient having a normal ***,no tumor progression was *** The combined transcranial and endoscopic endonasal approach is feasible for removing certain massive intracranial and extracranial communicating *** techniques,such as embolization,microscopic,and endoscopic manipulation,are indispensable tools for treating such cases.

关键词： meningioma transcranial approach endoscopic endonasal approach

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

WaveFuse: A unified unsupervised framework for image fusion with discrete wavelet transform

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arXiv 2020年

作者： Liu, Shaolei Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assited Intervention

We propose an unsupervised image fusion architecture for multiple application scenarios based on the combination of multi-scale discrete wavelet transform through regional energy and deep learning. To our best knowledge, this is the first time that a conventional frequency method has been combined with deep learning for feature maps fusion. The useful information of feature maps can be utilized adequately through multi-scale discrete wavelet transform in our proposed method. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation. Moreover, it's worth mentioning that comparable fusion performance trained in COCO dataset can be obtained by training with a much smaller dataset with only hundreds of images chosen randomly from COCO. Hence, the training time is shortened substantially, leading to the improvement of the model's performance both in practicality and training efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Image fusion

Estimating mean trabecular bone spacing based on the combination of Hilbert transform and fundamental frequency estimation

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Science Foundation in China 2017年第3期25卷 57-71页

作者：李颖徐峰刘成成许凯亮他得安王威琪 Department of Electronic Engineering Fudan UniversityShanghai 200433China Institute of Acoustics Tongji UniversityShanghai 200092China Sorbonne Université UPMC Univ Paris 06INSERM UMR-S 1146CNRS UMR 7371Laboratoired'ImagerieBiomédicaleF-75006ParisFrance State Key Laboratory of ASIC and System Fudan UniversityShanghai 200433China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention(MICCAI)of Shanghai Shanghai 200032China

Ultrasonic backscatter signals from cancellous bone are sensitive to the microstructure of trabecular bone,and thus enable the feasibility to extract microstructural information of trabecular *** mean trabecular bone spacing(MTBS)is an important parameter for characterizing bone *** paper proposes an MTBS estimation method based on the combination of Hilbert transform and fundamental frequency estimation(CHF). The CHF was verified with ultrasonic backscatter signals from simulations and in vitro measurements at a central frequency of *** CHF method was compared with the simplified inverse filter tracking(SIFT)method,Simons' Quadratic Transformation(QT)method,Singular Spectrum Analysis(SSA)method,and Spectral Autocorrelation(SAC)***-Carlo simulations were performed by varying the MTBS,signal-to-noise ratio(SNR),standard deviation of regular spacing(SDRS),amplitude ratio of diffuse scattering to regular scattering(Ad)and frequency dependent attenuation(nBUA).The simulation results showed that the CHF method had a better performance in MTBS estimation under almost all the examination conditions except for *** estimation percentage correct(EPC)was greater than 90% when the MTBS was in the range of 0.4to *** the in vitro measurements,the estimated EPC by the CHF method was91.25±7.81%(mean±standard deviation).A significant correlation was observed for the CHF-estimated MTBS and micro-computed tomography(μ-CT)-measured values(R^2=0.75,p<0.01).These results demonstrate that the CHF method is anti-interference for MTBS estimation and can be used to estimate trabecular bone spacing.

关键词： Ultrasonic backscatter Mean trabecular bone spacing estimation Hilbert transform and fundamental frequency estimation Cancellous bone microstructure

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

USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis

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arXiv 2023年

作者： Jiao, Jing Zhou, Jin Li, Xiaokang Xia, Menghua Huang, Yi Huang, Lihong Wang, Na Zhang, Xiaofan Zhou, Shichong Wang, Yuanyuan Guo, Yi Department of Electronic Engineering School of Information Science and Technology Fudan University Shanghai China Fudan University Shanghai Cancer Center Shanghai China Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States SenseTime Research Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundational models encounters intrinsic challenges in US analysis, i.e., insufficient databases, low quality, and ineffective features. In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis. First, a large-scale Multi-organ, Multi-center, and Multi-device US database was built, comprehensively containing over two million US images. Organ-balanced sampling was employed for unbiased learning. Then, USFM is self-supervised pre-trained on the sufficient US database. To extract the effective features from low-quality US images, we proposed a spatial-frequency dual masked image modeling method. A productive spatial noise addition-recovery approach was designed to learn meaningful US information robustly, while a novel frequency band-stop masking learning approach was also employed to extract complex, implicit grayscale distribution and textural variations. Extensive experiments were conducted on the various tasks of segmentation, classification, and image enhancement from diverse organs and diseases. Comparisons with representative US image analysis models illustrate the universality and effectiveness of USFM. The label efficiency experiments suggest the USFM obtains robust performance with only 20% annotation, laying the groundwork for the rapid development of US models in clinical practices. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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

超声背散射松质骨诊断仪及其在新生儿骨质评价中的应用

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Engineering 2015年第3期 123-137页

作者：刘成成张蓉李颖徐峰他得安王威琪 Department of Electronic Engineering Fudan University Department of Neonatology Children’s Hospital of Fudan University Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai

超声背散射技术在松质骨骨质无损评价中极具前景。本文介绍了一种新型超声背散射骨质诊断仪(UBBD)及其在新生儿骨质状况评价中的应用。超声背散射骨质诊断仪具有无损、无电离辐射、便携及操作简便等优点,可以在短时间内(5 s)获得人体松... 详细信息

超声背散射技术在松质骨骨质无损评价中极具前景。本文介绍了一种新型超声背散射骨质诊断仪(UBBD)及其在新生儿骨质状况评价中的应用。超声背散射骨质诊断仪具有无损、无电离辐射、便携及操作简便等优点,可以在短时间内(5 s)获得人体松质骨背散射信号。笔者共采集了467例新生儿(268男/199女)左侧跟骨处的背散射信号,所用探头中心频率为3.5 MHz。逐步调节背散射有效信号(SOI)的延迟(T1)和长度(T2),计算表观积分背散射(AIB)、表观背散射频率斜率(FSAB)及零频率截距(FIAB)和频谱质心偏移量(SCS)等参数。结果表明,背散射有效信号的选取对背散射测量有直接的影响。当T1较短时(<8μs),AIB和FIAB与新生儿胎龄有显著的正相关性(|R|=0.45,P<0.001);而当T1较长时(>10μs),AIB和FIAB负相关于新生儿胎龄(|R|=0.56,P<0.001),还可以观察到FSAB和SCS与新生儿胎龄有中度的正相关性(|R|=0.45,P<0.001)。T2对背散射测量的影响较小,引起相关系数的波动较小。本文通过自制的松质骨超声诊断仪证明超声背散射信号用于评价新生儿骨质状况的可行性。本文还提出了明确的背散射有效信号选取规则及新生儿骨质评价标准。

关键词：超声背散射松质骨评价有效信号背散射仪器新生儿骨质

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