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3D RoI-aware U-net for accurate and efficient colorectal tumor segmentation

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arXiv 2018年

作者： Huang, Yi-Jie Dou, Qi Wang, Zi-Xian Liu, Li-Zhi Jin, Ying Li, Chao-Feng Wang, Lisheng Chen, Hao Xu, Rui-Hua Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Imsight Medical Technology Co. Ltd. China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China

Segmentation of colorectal cancerous regions from 3D Magnetic Resonance (MR) images is a crucial procedure for radiotherapy which conventionally requires accurate delineation of tumour boundaries at an expense of labor, time and reproducibility. While deep learning based methods serve good baselines in 3D image segmentation tasks, small applicable patch size limits effective receptive field and degrades segmentation performance. In addition, Regions of interest (RoIs) localization from large whole volume 3D images serves as a preceding operation that brings about multiple benefits in terms of speed, target completeness, reduction of false positives. Distinct from sliding window or non-joint localization-segmentation based models, we propose a novel multitask framework referred to as 3D RoI-aware U-Net (3D RU-Net), for RoI localization and in-region segmentation where the two tasks share one backbone encoder network. With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detail-preserving segmentation and therefore enlarged applicable volume size and effective receptive field. To effectively train the model, we designed a Dice formulated loss function for the global-to-local multi-task learning procedure. Based on the efficiency gains demonstrated by the proposed method, we went on to ensemble models with different receptive fields to achieve even higher performance costing minor extra computational expensiveness. Extensive experiments were subsequently conducted on 64 cancerous cases with a four-fold cross-validation, and the results showed significant superiority in terms of accuracy and efficiency over conventional state-of-the art frameworks. In conclusion, the proposed method has a huge potential for extension to other 3D object segmentation tasks from medical images due to its inherent generalizability. The code for the proposed method is publicly available. Copyright ©

关键词： Signal encoding

Dictionary learning based statistical interior reconstruction without a prior knowledge 10

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Dictionary learning based statistical interior reconstructio...

Developments in X-Ray Tomography X

作者： Shi, Yongyi Mou, Xuanqin Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an Shaanxi710049 China

ISBN: (纸本)9781510603257

Despite the significantly practical utilities of interior tomography, it still suffers from severe degradation of direct current (DC) shift artifact. Existing literature suggest to introducing prior information of object support (OS) constraint or the zeroth order image moment, i.e., the DC value into interior reconstruction to suppress the shift artifact, while the prior information is not always available in practice. Aimed at alleviating the artifacts without prior knowledge, in this paper, we reported an approach on the estimation of the object support which could be employed to estimate the zeroth order image moment, and hence facilitate the DC shift artifacts removal in interior reconstruction. Firstly, by assuming most of the reconstructed object consists of soft tissues that are equivalent to water, we reconstructed a virtual OS that is symmetrical about the interior region of interest (ROI) for the DC estimation. Hence the DC value can be estimated from the virtual reconstruction. Secondly, a statistical iterative reconstruction incorporated with the sparse representation in terms of learned dictionary and the constraint in terms of image DC value was adopted to solve the interior tomography. Experimental results demonstrate that the relative errors of the estimated zeroth order image moment are 4.7% and 7.6%, corresponding to the simulated data of a human thorax and the real data of a sheep lung, respectively. Reconstructed images with the constraint of the estimated DC value exhibit greatly superior image quality to that without DC value constraint. © Copyright 2016 SPIE.

关键词： image segmentation

Scale-space anisotropic total variation for limited angle tomography

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arXiv 2017年

作者： Huang, Yixing Taubmann, Oliver Huang, Xiaolin Haase, Viktor Lauritsch, Guenter Maier, Andreas Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nuremberg Erlangen Germany Pattern Recognition Lab FriedrichAlexander-University Erlangen-Nuremberg Erlangen Germany Erlangen Germany Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Siemens Healthcare GmbH Forchheim Germany Department of Radiology University of Utah Salt Lake CityUT United States

This paper addresses streak reduction in limited angle tomography. Although the iterative reweighted total variation (wTV) algorithm reduces small streaks well, it is rather inept at eliminating large ones since total variation (TV) regularization is scale-dependent and may regard these streaks as homogeneous areas. Hence, the main purpose of this paper is to reduce streak artifacts at various scales. We propose the scale-space anisotropic total variation (ssaTV) algorithm in two different implementations. The first implementation (ssaTV-1) utilizes an anisotropic gradient-like operator which uses 2 · s neighboring pixels along the streaks' normal direction at each scale s. The second implementation (ssaTV-2) makes use of anisotropic down-sampling and up-sampling operations, similarly oriented along the streaks' normal direction, to apply TV regularization at various scales. Experiments on numerical and clinical data demonstrate that both ssaTV algorithms reduce streak artifacts more effectively and efficiently than wTV, particularly when using multiple scales. Copyright © 2017, The Authors. All rights reserved.

关键词： Anisotropy

Fault detection and isolation of sensor in Markov jump systems based on space geometry method

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Fault detection and isolation of sensor in Markov jump syste...

Chinese Control Conference (CCC)

作者： Yandong Hou Dianfeng Qiao Qianshuai Cheng Ruirui Huang Institute of Image Processing and Pattern Recognition Henan University Kaifeng Henan CN

This paper investigates sensor fault problems in Markov jump systems with uncertain disturbances. Using the measurement equation, the sensor faults can be translated into the state inputs. Subsequently, a cluster of residual generators is designed by employing the space geometric method. The corresponding filter parameters are obtained based on the space geometric approach, then using H ∞ optimization technique reduce the effects of disturbance inputs on the residuals, at the same time, the residual generator is designed so that the residual signals and sensor faults satisfy one to one correspondence, which can be used to detect and isolate the sensor faults in Markov jump systems with disturbance. Simulation results demonstrate the efficiency of the proposed method.

关键词： Hafnium Decision support systems

Fault detection and isolation of sensor in time-delay systems based on space geometry method

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Fault detection and isolation of sensor in time-delay system...

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Dianfeng Qiao Qianshuai Cheng Yandong Hou Institute of Image Processing and Pattern Recognition Henan University Kaifeng China

ISBN: (纸本)9781467386456

This paper investigates sensor fault problems in time-delay systems with uncertain disturbances. Using the measurement equation, the sensor fault can be translated into the state inputs. Subsequently, a cluster of residual generators is designed by employing the space geometric method. The corresponding filter parameters are obtained based on the space geometric approach, then using H ∞ optimization technique reduce the effects of disturbance inputs on the residuals, at the same time, the residual generator is designed so that the residual signals and sensor faults satisfy one to one correspondence, which can be used to detect and isolate the sensor faults in time-delay systems with disturbance. Simulation results suggest the effectiveness and robustness of our proposed approach.

关键词： Conferences Industrial electronics Decision support systems Fault detection Optimization

Fast KNN search for big data with set compression tree and best bin first 2

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Fast KNN search for big data with set compression tree and b...

2nd International Conference on Cloud Computing and Internet of Things, CCIOT 2016

作者： Chen, Zhenjie Yan, Jingqi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of Ministry of Education for System Control and Information Processing China Shanghai China

ISBN: (纸本)9781467398213

This paper proposes k nearest neighbors (kNN) search based on set compression tree (SCT) and best bin first (BBF) to deal with the problem for big data. The large compression rate by set compression tree is achieved by compressing the set of descriptors jointly instead of compressing on a per-descript or basis. So set compression tree has a good performance in kNN search at a low bit rate. At the same time, the best bin first (BBF) is a very efficient algorithm to find the approximately kNN from a large number of high dimensional feature descriptors. SCT-BBF is a novel exploration and it improves search performance in three aspects: First, SCT-BBF requires less memory footprint, which is important in big data age. Second, it increases accuracy compared traditional method like KD-Tree and original SCT. SCT-BBF can be used with other data processing methods like PCA and SIFT to perform better. Third, this paper adopts finer search to increase accuracy at a slight loss of speed. And it can extend to big data easily. © 2016 IEEE.

关键词： Big data

Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks

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arXiv 2018年

作者： Qin, Yulei Wen, Juan Zheng, Hao Huang, Xiaolin Yang, Jie Song, Ning Zhu, Yue-Min Wu, Lingqian Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Center for Medical Genetics School of Life Sciences Central South University Changsha410078 China Shanghai Key Laboratory of Reproductive Medicine School of Medicine Shanghai Jiao Tong University Shanghai200025 China Diagens-Hangzhou Hangzhou311121 China University Lyon INSA Lyon CNRS INSERM CREATIS UMR 5220 U1206F-69621 France Hamlyn Centre for Robotic Surgery Imperial College London SW72AZ United Kingdom

Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosomes type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one local-scale network (L-Net). It follows three stages. The first stage is to learn both global and local features. We extract global features and detect finer local regions via the G-Net. By proposing a varifocal mechanism, we zoom into local parts and extract local features via the L-Net. Residual learning and multi-task learning strategies are utilized to promote high-level feature extraction. The detection of discriminative local parts is fulfilled by a localization subnet of the G-Net, whose training process involves both supervised and weakly-supervised learning. The second stage is to build two multi-layer perceptron classifiers that exploit features of both two scales to boost classification performance. The third stage is to introduce a dispatch strategy of assigning each chromosome to a type within each patient case, by utilizing the domain knowledge of karyotyping. Evaluation results from 1909 karyotyping cases showed that the proposed Varifocal-Net achieved the highest accuracy per patient case (%) of 99.2 for both type and polarity tasks. It outperformed state-of-the-art methods, demonstrating the effectiveness of our varifocal mechanism, multi-scale feature ensemble, and dispatch strategy. The proposed method has been applied to assist practical karyotype diagnosis. Copyright © 2018, The Authors. All rights reserved.

关键词： Computer aided diagnosis

Multi-stage suture detection for robot assisted anastomosis based on deep learning

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arXiv 2017年

作者： Hu, Yang Gu, Yun Yang, Jie Yang, Guang-Zhong Hamlyn Centre for Robotic Surgery Imperial College London SW72AZ United Kingdom School of Biomedical Engineering Institute of Image Processing & Pattern Recognition Shanghai Jiao Tong University Shanghai China

In robotic surgery, task automation and learning from demonstration combined with human supervision is an emerging trend for many new surgical robot platforms. One such task is automated anastomosis, which requires bimanual needle handling and suture detection. Due to the complexity of the surgical environment and varying patient anatomies, reliable suture detection is difficult, which is further complicated by occlusion and thread topologies. In this paper, we propose a multi-stage framework for suture thread detection based on deep learning. Fully convolutional neural networks are used to obtain the initial detection and the overlapping status of suture thread, which are later fused with the original image to learn a gradient road map of the thread. Based on the gradient road map, multiple segments of the thread are extracted and linked to form the whole thread using a curvilinear structure detector. Experiments on two different types of sutures demonstrate the accuracy of the proposed framework. Copyright © 2017, The Authors. All rights reserved.

关键词： Robotic surgery

Cerebral ASL, SPECT and MRI image registration, enhancing fusion and joint analysis system

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Cerebral ASL, SPECT and MRI image registration, enhancing fu...

2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Ye Liu Yang Wang Lisheng Wang Shaoli Song Institute of Image Processing and Pattern Recognition Department of AutomationShanghai Jiao Tong University Department of Nuclear Medicine Ren Ji HospitalSchool of MedicineShanghai Jiao tong University

ISBN: (纸本)9781509001668

There is no technique to do a joint analysis of Arterial Spin Labeling(ASL),Single Photon Emission Computed Tomography(SPECT) and Magnetic Resonance Imaging(MRI) *** this paper,we propose a method to realize multimodality image registration,enhancing fusion and features calculation of 3D volume of *** mutual information registration,VTK image fusion,pseudo-color mapping,spline interpolation,3D tomographic interpolation,region growth and other algorithms,we build a joint analysis *** test results show that this system provides accurate registration function and more distinguishable images for the ***,gray average of pathological brain area is calculated as regional cerebral blood flow(r CBF),which provides the basis for clinicians to make confirmed diagnoses.

关键词： ASL SPECT MRI Registration Enhancing fusion Volume of interest Cerebral blood flow