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30th IEEE International Conference on Image Processing (ICIP)

作者： Wang, Yifan Deng, Siyuan Yuan, Kunhao Schaefer, Gerald Liu, Xiyao Fang, Hui Loughborough Univ Dept Comp Sci Loughborough Leics England Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9781728198354

Class activation maps (CAMs) have emerged as a popular technique to improve model interpretability of deep learning-based models. While existing CAM methods are able to extract salient semantic regions to provide high-confidence pseudo-labels for downstream tasks such as semantic segmentation, they are less effective when dealing with multi-object scenes. In this paper, we design a multi-channel weight assignment scheme that learns from both positive and negative regions to yield an improved CAM model for images comprising multiple objects. We demonstrate the effectiveness of our proposed method on two new data sets, a cat-and-dog dataset and a PASCAL VOC 2012-based multi-object dataset, and show it to compare favourably with other state-of-the-art CAM methods, outperforming them in terms of both mIoU and inter-object activation ratio (IAR), a new evaluation measure proposed to evaluate CAM performance in multi-object scenes.

关键词： deep learning models class activation map model interpretability multi-object segmentation

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multi-target segmentation of pancreas and pancreatic tumor based on fusion of attention mechanism

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第Part2期79卷 000-000页

作者： Cao, Luyang Li, Jianwei Chen, Shu Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350108 Peoples R China

Existing neural network segmentation schemes perform well in the task of segmenting images of organs with large areas and clear morphology, such as the liver and lungs. However, it is difficult to segment organs with variable morphology and small target area, such as pancreas and tumors. In order to achieve accurate seg-mentation of pancreas and its cysts, MDAG-Net (multi-dimensional Attention Gate Network) is proposed in this paper. Combining three attention mechanisms: spatial, channel and multi-dimensional feature map input, MDAG (multi-dimensional Attention Gate) obtains the global distribution of semantic information in spatial and channel dimensions, filters redundant information in shallow feature maps, realizes feature response, and recalibrates convolution kernel parameters. In addition, the WML(Weighted cross entropy and MIoU loss function) loss can adaptively assign the weight of category loss and count the classification error of global pixels, which can in-crease the error attention of the target area and improving the segmentation accuracy of the network. The al-gorithm is experimented on the Task07_Pancreas dataset, compared with U-Net under the same conditions, the Dice coefficient, Precision, Recall rate and MIoU (Mean Intersection over Union) of MDAG-Net are improved by 5.3%, 1.5%, 12.7% and 7.6% respectively. The results show that MDAG-Net can accurately segment the region of pancreas and its cyst in CT(Computed Tomography) images, which proves that MDAG has better segmentation efficiency for such small target regions.

关键词： Pancreas Pancreatic tumor multi-object segmentation Attention mechanism Small target detection Cross entropy loss

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multi-layer Fast Level Set segmentation for Macular OCT 15

Multi-layer Fast Level Set Segmentation for Macular OCT

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15th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Liu, Yihao Carass, Aaron Solomon, Sharon D. Saidha, Shiv Calabresi, Peter A. Prince, Jerry L. Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Univ Wilmer Eye Inst Sch Med Baltimore MD 21287 USA Johns Hopkins Univ Dept Neurol Sch Med Baltimore MD 21287 USA

ISBN: (纸本)9781538636367

Segmenting optical coherence tomography (OCT) images of the retina is important in the diagnosis, staging, and tracking of ophthalmological diseases. Whereas automatic segmentation methods are typically much faster than manual segmentation, they may still take several minutes to segment a three-dimensional macular scan, and this can be prohibitive for routine clinical application. In this paper, we propose a fast, multi-layer macular OCT segmentation method based on a fast level set method. In our framework, the boundary evolution operations are computationally fast, are specific to each boundary between retinal layers, guarantee proper layer ordering, and avoid level set computation during evolution. Subvoxel resolution is achieved by reconstructing the level set functions after convergence. Experiments demonstrate that our method reduces the computation expense by 90% compared to graph-based methods and produces comparable accuracy to both graph-based and level set retinal OCT segmentation methods.

关键词： fast level set method OCT multi-object segmentation topology preservation

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multi-resolution multi-object statistical shape models based on the locality assumption

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MEDICAL IMAGE ANALYSIS 2017年 38卷 17-29页

作者： Wilms, Matthias Handels, Heinz Ehrhardt, Jan Univ Lubeck Inst Med Informat Ratzeburger Allee 160 D-23538 Lubeck Germany

Statistical shape models learned from a population of previously observed training shapes are nowadays widely used in medical image analysis to aid segmentation or classification. However, providing an appropriate and representative training population of preferably manual segmentations is typically either very labor-intensive or even impossible. Therefore, statistical shape models in practice frequently suffer from the high-dimension-low-sample-size (HDLSS) problem resulting in models with insufficient expressiveness. In this paper, a novel approach for learning representative multi-resolution multi-object statistical shape models from a small number of training samples that adequately model the variability of each individual object as well as their interrelations is presented. The method is based on the assumption of locality, which means that local shape variations have limited effects in distant areas and, therefore, can be modeled independently. This locality assumption is integrated into the standard statistical shape modeling framework by manipulating the sample covariance matrix (non-zero covariances between distant landmarks are set to zero). To allow for multi-object modeling, a method for computing distances between points located on different object shapes is proposed. Furthermore, different levels of locality are introduced by deriving a multi-resolution scheme, which is equipped with a method to combine variability information modeled at different levels into a single shape model. This combined representation of global and local variability in a single shape model allows the use of the classical active shape model strategy for model-based image segmentation. An extensive evaluation based on a public data base of 247 chest radiographs is performed to show the modeling and segmentation capabilities of the proposed approach in single- and multi-object HDLSS scenarios. The new approach is not only compared to the classical shape modeling method but also to

关键词： Statistical shape models multi-resolution multi-object segmentation High-dimension-low-sample-size problem

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Real-time automatic multilevel color video thresholding using a novel class-variance criterion

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MACHINE VISION AND APPLICATIONS 2015年第2-3期26卷 233-249页

作者： Tsai, Chi-Yi Liu, Tsung-Yen Tamkang Univ Dept Elect Engn New Taipei City 25137 Taiwan

Color image segmentation is a crucial preliminary task in robotic vision systems. This paper presents a novel automatic multilevel color thresholding algorithm to address this task efficiently. The proposed algorithm consists of a learning process and a multi-threshold searching process. The learning process learns the color distribution of an input video sequence in HSV color space, and the multi-threshold searching process automatically determines the optimal multiple thresholds to segment all colors-of-interest in the video based on a novel class-variance criterion. For the learning process, a simple and efficient color-distribution learning algorithm operating with a color-pixel extraction method is proposed to learn a color distribution model of all colors-of-interest in the video images, which simplifies the search for optimal thresholds for the colors-of-interest through a conventional multilevel thresholding method. For the multi-threshold searching process, a nonparametric multilevel color thresholding algorithm with an extended within-class variance criterion is proposed to automatically find the optimal upper bound and lower bound threshold values of each color channel. Experimental results validate the performance and computational efficiency of the proposed method by comparing with three existing methods, both visually and quantitatively.

关键词： multi-object segmentation Nonparametric multilevel color thresholding Extended within-class variance Automatic multi-threshold searching

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Automatic Anatomy Recognition in Post-Tonsillectomy MR images of obese children with OSAS

Automatic Anatomy Recognition in Post-Tonsillectomy MR image...

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Computer-Aided Diagnosis (CAD) Conference at the SPIE Medical Imaging Symposium

作者： Tong, Yubing Udupa, Jayaram K. Odhner, Dewey Sin, Sanghun Arens, Raanan Univ Penn Dept Radiol Med Image Proc Grp Philadelphia PA 19104 USA Albert Einstein Coll Med Childrens Hosp Montefiore Bronx NY 10467 USA

ISBN: (纸本)9781628415049

Automatic Anatomy Recognition (AAR) is a recently developed approach for the automatic whole body wide organ segmentation. We previously tested that methodology on image cases with some pathology where the organs were not distorted significantly. In this paper, we present an advancement of AAR to handle organs which may have been modified or resected by surgical intervention. We focus on MRI of the neck in pediatric Obstructive Sleep Apnea Syndrome (OSAS). The proposed method consists of an AAR step followed by support vector machine techniques to detect the presence/absence of organs. The AAR step employs a hierarchical organization of the organs for model building. For each organ, a fuzzy model over a population is built. The model of the body region is then described in terms of the fuzzy models and a host of other descriptors which include parent to offspring relationship estimated over the population. Organs are recognized following the organ hierarchy by using an optimal threshold based search. The SVM step subsequently checks for evidence of the presence of organs. Experimental results show that AAR techniques can be combined with machine learning strategies within the AAR recognition framework for good performance in recognizing missing organs, in our case missing tonsils in post-tonsillectomy images as well as in simulating tonsillectomy images. The previous recognition performance is maintained achieving an organ localization accuracy of within 1 voxel when the organ is actually not removed. To our knowledge, no methods have been reported to date for handling significantly deformed or missing organs, especially in neck MRI.

关键词： multi-object segmentation missing organs detection Automatic Anatomy Recognition-AAR Adenotonsillectomy

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A Novel Histogram-Based multi-Threshold Searching Algorithm for multilevel Colour Thresholding

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2012年第5期9卷

作者： Tsai, Chi-Yi Liu, Tsung-Yen Chen, Wei-Chieh Tamkang Univ Dept Elect Engn New Taipei City Taiwan

Image segmentation is an important preliminary process required in object tracking applications. This paper addresses the issue of unsupervised multi-colour thresholding design for colour-based multiple objects segmentation. Most of the current unsupervised colour thresholding techniques require adopting a supervised training algorithm or a cluster-number decision algorithm to obtain optimal threshold values of each colour channel for a colour-of-interest. In this paper, a novel unsupervised multi-threshold searching algorithm is proposed to automatically search the optimal threshold values for segmenting multiple colour objects. To achieve this, a novel ratio-map image computation method is proposed to efficiently enhance the contrast between colour and non-colour pixels. The Otsu's method is then applied to the ratio-map image to extract all colour objects from the image. Finally, a new histogram-based multi-threshold searching algorithm is developed to search the optimal upper-bound and lower-bound threshold values of hue, saturation and brightness components for each colour object. Experimental results show that the proposed method not only succeeds in separating all colour objects-of-interest in colour images, but also provides satisfactory colour thresholding results compared with an existing multilevel thresholding method.

关键词： multi-object segmentation multilevel Thresholding Colour Thresholding multi-threshold Searching Ratio-map Image

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Disparity contour grouping for multi-object segmentation in dynamically textured scenes

Disparity contour grouping for multi-object segmentation in ...

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2nd International Conference on Computer Graphics Theory and Applications/2nd International Conference on Computer Vision Theory and Applications

作者： Sun, Wei Spackman, Stephen P. Hewlett Packard Labs 1501 Page Mill Rd Ms 1181 Palo Alto CA 94304 USA Timespring Software Corp Montreal PQ H3A 2W5 Canada

ISBN: (纸本)9789728865740

A fast multi-object segmentation algorithm based on disparity contour grouping is described. It segments multiple objects at a wide range of depths from backgrounds of known geometry in a manner insensitive to changing lighting and the dynamic texture of, for example, display surfaces. Not relying on stereo reconstruction or prior knowledge of foreground objects, it is fast enough on commodity hardware for some real-time applications. Experimental results demonstrate its ability to extract object contour from a complex scene and distinguish multiple objects even when they are close together or partially occluded.

关键词： multi-object segmentation stereo matching background model disparity verification disparity contours

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Shape Degeneration and multi-object Search Basing on multi-shape Priors

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2011年第4期4卷 467-475页

作者： Song Chunhe Zhao Hai Jing Wei Zhu Hongbo Northeastern Univ Coll Informat Sci & Technol Shenyang 110819 Peoples R China Northeastern Univ Coll Software Shenyang 110819 Peoples R China

This paper proposed a framework of segmenting multiple targets basing on multiple shape priors. The key novel idea in the proposed framework is the shape degeneration model. By breaking the balance between the competition regions adaptively, the proposed model can degenerate the region which is with low similarity with any of the shape priors, until this region completely disappears. Meanwhile the proposed model can also merge the regions that belong to a single object automatically, which is especially important when some of objects are in partial occlusion. Additionally, we presented two new models in the proposed framework. One is a new local model of active contour, which can segment image with intensity inhomogeneity correctly. The other is a generalized shape prior model, which extends the intrinsic alignment model, and can take extra affine parameters into account in the alignment process of shape priors. Experimental results show the validity of the proposed method.

关键词： image segmentation level set shape priors multi-object segmentation

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A novel framework for segmentation of deep brain structures based on Markov dependence tree

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NEUROIMAGE 2009年第4期46卷 1027-1036页

作者： Wu, Jue Chung, Albert C. S. Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Lo Kwee Seong Med Image Anal Lab Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Bioengn Program Hong Kong Hong Kong Peoples R China

The aim of this work is to develop a new framework for multi-object segmentation of deep brain structures (caudate nucleus, putamen and thalamus) in medical brain images. Deep brain segmentation is difficult and challenging because the structures of interest are of relatively small size and have significant shape variations. The structure boundaries may be blurry or even missing, and the surrounding background is full of irrelevant edges. To tackle these problems, we propose a template-based framework to fuse the information of edge features, region statistics and inter-structure constraints for detecting and locating all target brain structures Such that initialization by hand is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree (MDT), and multiple objects are efficiently matched to a target image by a top-to-down optimization strategy. The final segmentation is obtained through refinement by a B-spline based non-rigid registration between the exemplar image and the target image. Our approach needs only one example as training data. We have validated the proposed method on a publicly available T1-weighted magnetic resonance image database with expert-segmented brain structures. in the experiments, the proposed approach has obtained encouraging results with 0.80 Dice score for the caudate nuclei, 0.81 Dice score for the putamina and 0.84 Dice score for the thalami on average. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Deep brain structures Caudate nucleus Putamen Thalamus multi-object segmentation Markov dependence tree

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