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Guiding image segmentation on the Fly: interactive segmentation From a Feedback Control Perspective

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2018年第10期63卷 3276-3289页

作者： Zhu, Liangjia Karasev, Peter Kolesov, Ivan Sandhu, Romeil Tannenbaum, Allen SUNY Stony Brook Dept Comp Sci & Appl Math Stat Stony Brook NY 11794 USA Georgia Inst Technol Atlanta GA 30332 USA Agilent Technol Santa Clara CA 95051 USA

image segmentation is a fundamental problem in computational vision and medical imaging. Designing a generic automated method that works for various objects and imaging modalities is a formidable task. Instead of proposing a new specific segmentation algorithm, we present a general design principle on how to integrate user interactions from the perspective of feedback control theory. Lyapunov stability analysis is employed to design and analyze an interactive segmentation system. Then, stabilization conditions are derived to guide the algorithm design. Finally, the effectiveness and robustness of the proposed method are demonstrated.

关键词： Dynamical system evolutionary process feedback control hybrid systems interactive image segmentation

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Fast interactive segmentation algorithm of image sequences based on relative fuzzy connectedness

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Journal of Systems Engineering and Electronics 2005年第4期16卷 750-755页

作者： Tian Chunna Gao Xinbo School of Electronic Engineering Xidian Univ. Xi'an 710071 P. R. China

A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the original algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex hackground and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.

关键词： fuzzy connectedness interactive image segmentation image-sequences segmentation multiple objects segmentation fast algorithm.

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A Novel Level Set Model for image segmentation with interactive Label Regularization Term 2nd

A Novel Level Set Model for Image Segmentation with Interact...

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2nd CCF Chinese Conference on Computer Vision (CCCV)

作者： Tan, Huang Chen, Ming Li, Qiaoliang Qu, Shaojun Hunan Normal Univ Coll Math & Comp Sci Minist Educ China Key Lab High Performance Comp & Stochast Informat Changsha 410081 Hunan Peoples R China

ISBN: (纸本)9789811073052;9789811073045

We propose an interactive level set segmentation method with a novel user's label regularization term. This new edge-based model can force the evolution of level set function to follow the hard constraints given by the user and effectively solve the problem that user can't extract some specified object in the image due to the local optimum. The new regularization term is constructed by multiplication of the hard constraints and the level set function. Since the regularization term we defined only works on the pixels labeled by the user, the evolution of level set function can be affected accurately by user's label for foreground and background. Experimental results are provided to demonstrate the efficiency and accuracy of the new model. Our method can make the segmentation accurately reflect the user's label. The new method supports the real-time feedback in the segmentation process. We also analyzed the weight of the regularization term, and the best weight of the regularization term is provided.

关键词： interactive image segmentation Real-time feedback Level set Regularization term

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Progressive medical image annotation with convolutional neural network-based interactive segmentation method

Progressive medical image annotation with convolutional neur...

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Conference on Medical Imaging - image Processing

作者： Bai, Yunkun Sun, Guangmin Li, Yu Le Shen Li Zhang Beijing Univ Technol Fac Informat Technol 100 PingLeYuan Beijing 100124 Peoples R China Tsinghua Univ Minist Educ Key Lab Particle & Radiat Imaging Beijing Peoples R China Tsinghua Univ Dept Engn Phys Beijing 100084 Peoples R China

ISBN: (纸本)9781510640221

Deep learning based segmentation algorithms for medical image require massive training datasets with accurate annotations, which is costly since it takes much human effort to manually labeling from scratch. Therefore, interactive image segmentation is important and may greatly improve the efficiency and accuracy of medical image labeling. Some interactive segmentation methods (e.g. Deep Extreme Cut and Deepgrow) may improve the labeling through minimal interactive input. However these methods only utilize the initial manually input information, while existing segmentation results (such as annotations produced by nonprofessionals or conventional segmentation algorithms) cannot be utilized In this paper, an interactive segmentation method is proposed to make use of both existing segmentation results and human interactive information to optimize the segmentation results progressively. In this framework, the user only needs to click on the foreground or background of the target individual on the medical image, the algorithm could adaptively learn the correlation between them, and automatically completes the segmentation of the target. The main contributions of this paper are: (1) We adjusted and applied a convolutional neural network which takes medical image data and user's clicks information as input to achieve more accurate segmentation of medical images. (2) We designed an iterative training strategy to realize the applicability of the model to deal with different number of clicks data input. (3) We designed an algorithm based on false positive and false negative regions to simulate the user's clicks, so as to provide enough training data. By applying the proposed method, users can easily extract the region of interest or modify the segmentation results by multiple clicks. The experimental results of 6 medical image segmentation tasks show that the proposed method achieves more accurate segmentation results by at most five clicks.

关键词： medical image annotation interactive image segmentation convolutional neural network

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interactive Deep Editing Framework for Medical image segmentation 1

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Zhou, Bowei Chen, Li Wang, Zhao Tsinghua Univ Sch Software Beijing 100084 Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China

ISBN: (数字)9783030322489

ISBN: (纸本)9783030322489;9783030322472

Deep neural networks exhibit superior performance in dealing with segmentation of 3D medical images. However, the accuracy of segmentation results produced by fully automatic algorithms is insufficiently high due to the complexity of medical images;as such, further manual editing is required. To solve this problem, this paper proposes an interactive editing method combined with 3D end-to-end segmentation network. In the training stage, we simulate the user interactions, which are used as training data, by comparing the segmentation automatically generated by convolutional neural network with the ground truth. User interactions are fed into the network along with the images, allowing the network to adjust the segmentation results based on user edits. Our system provides three editing tools for smartly fixing segmentation errors, which cover most commonly used editing styles in medical image segmentation. With the high-level semantic information in the network, our method can efficiently and accurately edit the 3D segmentation. The interactive editing experiments on the BraTS dataset show that our method can significantly improve the segmentation accuracy with a small number of interactions only. The proposed method presents potential for clinical applications.

关键词： interactive image segmentation Medical image segmentation Deep learning

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interactive Skin Lesion segmentation Considering Behavioral Preference in Clicking

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第1期20卷 89-100页

作者： Zhao, Shuofeng Gu, Chunzhi Yu, Jun Akashi, Takuya Zhang, Chao Univ Fukui Dept Engn 3-9-1 Bunkyo Fukui Fukui 9502181 Japan Toyohashi Univ Technol Dept Comp Sci & Engn 1-1 HibarigaokaTempaku Cho Toyohashi Aichi 4418580 Japan Niigata Univ Inst Sci & Technol 8050 Ikarashi 2 No ChoNishi Ku Niigata Niigata 9502181 Japan Okayama Univ Sch Engn 2-1-1 TsushimanakaKita Ku Okayama 7008530 Japan Univ Toyama Fac Engn 3190 Gofuku Toyama 9308555 Japan

interactive Medical image segmentation (IMIS) aims to improve the accuracy of image segmentation by incorporating human guidance, primarily through click-based interactions. IMIS for skin lesion segmentation is a challenging task because the edges of lesion regions on the skin are often ambiguous, and training IMIS models requires the generation of pseudo-clicks to simulate human clicks. Most previous methods generate pseudo-clicks by sampling from the entire mis-segmented region. However, such clicks are inconsistent with human behavior, resulting in performance degradation, particularly for skin lesion segmentation. In this study, we address this issue by integrating human preference into the process of generating pseudo clicks to train the segmentation model, which is simple yet effective. Specifically, through a user study, we find that people are more inclined to click on larger mis-segmented regions during interactive segmentation. Inspired by this, a roulette selection strategy is used to generate the pseudo-clicks based on the area of the mis-segmented subregions. Our proposed method, BehaviorClick, can be easily integrated with existing interactive segmentation models to improve the performance. The accuracy improvement on four dermoscopic datasets under six state-of-the-art interactive segmentation methods is confirmed, which demonstrates the generalizability and effectiveness of our approach. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： skin lesion image segmentation user behavior interactive image segmentation

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interactive Exemplar-based segmentation Toolkit for Biomedical image Analysis 12

Interactive Exemplar-based Segmentation Toolkit for Biomedic...

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IEEE 12th International Symposium on Biomedical Imaging

作者： Li, Xiang Zhou, Zhi Keller, Philipp Zeng, Hongkui Liu, Tianming Peng, Hanchuan Univ Georgia Dept Comp Sci Cort Architecture Imaging & Discovery Lab Athens GA 30602 USA Univ Georgia Bioimaging Res Ctr Athens GA 30602 USA Allen Inst Brain Sci Seattle WA 98109 USA HHMI Janelia Farm Res Campus Ashburn VA USA

ISBN: (纸本)9781479923748

In the field of biomedical imaging analysis on single-cell level, reliable and fast segmentation of the cell nuclei from the background on three-dimensional images is highly needed for the further analysis. In this work we propose an interactive cell segmentation toolkit that first establishes a set of exemplar regions from user input through an easy and intuitive interface in both 2D and 3D in real-time, then extracts the shape and intensity features from those exemplars. Based on a local contrast-constrained region growing scheme, each connected component in the whole image would be filtered by the features from exemplars, forming an "exemplar-matching" group which passed the filtering and would be part of the final segmentation result, and a "non-exemplar-matching" group in which components would be further segmented by the gradient vector field based algorithm. The results of the filtering process are visualized back to the user in near real-time, thus enhancing the experience in exemplar selecting and parameter tuning. The toolkit is distributed as a plugin within the open source Vaa3D system (http://***).

关键词： interactive image segmentation learning based approach

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interactive fuzzy connectedness image segmentation for neonatal brain MR image segmentation

Interactive fuzzy connectedness image segmentation for neona...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Kobashi, Syoji Kuramoto, Kei Hata, Yutaka Univ Hyogo Grad Sch Engn Himeji Initiat Computat Med & Hlth Technol Himeji Hyogo 6712201 Japan

ISBN: (纸本)9781479906529

image segmentation plays a fundamental work to analyze medical images. Although many literatures studied automated image segmentation, it is still difficult to segment region-of-interest in any kind of images. Thus, manual delineation is important yet. In order to shorten the processing time and to decrease the effort of users, this paper introduces two approaches of interactive image segmentation method based on fuzzy connectedness image segmentation (FCIS). The first approach interactively updates object affinity of FCIS according to users' additional seed voxels. The second approach models the profile of the object affinity using radial-basis function network (RBFN), and applies online training for users' additional seed voxels. The proposed methods updates segmentation results for not only the seed voxels but also the other miss-classified voxels. The methods had been applied to neonatal brain magnetic resonance (MR) images. The experimental results showed the second approach produced the best results.

关键词： interactive image segmentation fuzzy connectedness image segmentation radial-basis-function network neonatal brain magnetic resoance images

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interactive RGB-D image segmentation Using Hierarchical Graph Cut and Geodesic Distance 16th

Interactive RGB-D Image Segmentation Using Hierarchical Grap...

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16th Pacific-Rim Conference on Multimedia (PCM)

作者： Ge, Ling Ju, Ran Ren, Tongwei Wu, Gangshan Nanjing Univ Collaborat Innovat Ctr Novel Software Technol & I State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9783319240756;9783319240749

In this paper, we propose a novel interactive image segmentation method for RGB-D images using hierarchical Graph Cut. Considering the characteristics of RGB channels and depth channel in RGB-D image, we utilize Euclidean distance on RGB space and geodesic distance on 3D space to measure how likely a pixel belongs to foreground or background in color and depth respectively, and integrate the color cue and depth cue into a unified Graph Cut framework to obtain the optimal segmentation result. Moreover, to overcome the low efficiency problem of Graph Cut in handling high resolution images, we accelerate the proposed method with hierarchical strategy. The experimental results show that our method outperforms the state-of-the-art methods with high efficiency.

关键词： interactive image segmentation RGB-D image Graph cut Geodesic distance Scale space

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FISMI-DRL: A Framework for interactive segmentation of Medical image Based On Deep Reinforcement Learning

FISMI-DRL: A Framework for Interactive Segmentation of Medic...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Qian, Chen Yang, Hao Li, Jiyun School of Computer Sci. & Tech. Donghua University Shanghai China

ISBN: (纸本)9798350337020

At present, deep learning-based medical image segmentation algorithms have achieved fast and accurate semantic segmentation. However, their segmentation accuracy is still challenging to reach the clinical use standard, requiring further refinement by medical experts. Therefore, some researchers have turned their attention to interactive segmentation methods, which introduce human interaction to obtain information gain. Such methods model the dynamics of the image annotation process state and can effectively improve the segmentation accuracy under the interaction of medical experts. In this paper, we put forward a novel framework for the interactive segmentation of medical images based on deep reinforcement learning, namely FISMI-DRL. The experimental results demonstrate that our model achieves high segmentation accuracy and interaction efficiency. © 2023 IEEE.

关键词： deep learning interactive image segmentation reinforcement learning

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