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Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference

作者： Shuofeng Zhao Chunzhi Gu Jun Yu Chao Zhang University of Fukui Japan Toyohashi University of Technology Japan Niigata University Japan

ISBN: (纸本)9798400716225

Click-based interactive segmentation is a fundamental task in computer vision that allows user clicks to refine the results. Existing works typically focus on developing powerful segmentation models, yet sparsely treating the clicking method itself. In this study, we propose a novel clicking strategy that specifically aims to reflect the inter-individual variations of different humans during training to improve segmentation results. Our method consists of three steps. In particular, we first apply the erosion operation on the ground-truth segmentation mask with different parameter settings to generate multiple eroded masks. These eroded masks are then regarded as possible hypotheses of users’ interested regions. Then, we randomly select one mask from the hypotheses to simulate an arbitrary users’ behavior. By next solving the visual center of the selected mask, the training clicks are eventually obtained via randomly sampling from the visual center region. In essence, our key idea is to cast multiple eroded regions as the potentially diverse users’ interests, and include the resulting stochasticity into the model training for better generality. We directly adopt an existing segmentation backbone and incorporate our clicking strategy in the training to show the effectiveness of our method. Experimental results on five datasets generally demonstrate that our method contributes to state-of-the-art segmentation performance.

关键词： Click strategies Human Visual Psychology Inter-Individual variation interactive image segmentation segmentation

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A SYSTEMATIC PERFORMANCE EVALUATION OF interactive image segmentation METHODS BASED ON SIMULATED USER INTERACTION

A SYSTEMATIC PERFORMANCE EVALUATION OF INTERACTIVE IMAGE SEG...

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IEEE International Symposium on Biomedical Imaging

作者： Emmanouil Moschidis Jim Graham Imaging Science and Biomedical Engineering School of Cancer and Enabling Sciences Stopford Building The University of Manchester

ISBN: (纸本)9781424441259

In this paper we report on the results of a systematic performance evaluation of three efficient image segmentation algorithms, namely Graph-Cuts, Random-Walker and Grow-Cut. The evaluation focuses on their function as the computational part of an interactive segmentation system. The implications caused by the human involvement in the overall process are avoided by simulating two different patterns of user interaction. The methods are evaluated with respect to accuracy, precision, efficiency and parameter sensitivity on three dimensional medical images. The results provide useful insight regarding the algorithmic performance of the selected techniques and the effect of the identified patterns of user interaction on the segmentation outcome.

关键词： Performance Evaluation interactive image segmentation Simulated User Interaction 3-D Medical images Graph-Cuts Random-Walker Grow-Cut

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interactive Multilabel image segmentation via Robust Multilayer Graph Constraints

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IEEE TRANSACTIONS ON MULTIMEDIA 2016年第12期18卷 2358-2371页

作者： Wang, Tao Ji, Zexuan Sun, Quansen Chen, Qiang Jing, Xiao-Yuan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Automat Nanjing 210094 Jiangsu Peoples R China

The combination of pixel and superpixel has been widely utilized in the interactive segmentation methods to overcome the sensitivity to the seeds' quantity and quality. However, because of the introduction of more variables and variables' interactions, the pixel-superpixel combination methods are still limited to the segmentation accuracy and computational complexity. To solve these problems, in this paper, we propose an interactive multilabel image segmentation method. In the proposed segmentation model, the multilayer relationships among the pixel layer, superpixel layer, and label layer are fused by the Markov random field framework to further improve the segmentation accuracy. During the optimization stage, the parallel partial optimality strategy is utilized to effectively solve the multilabel submodular energy function. Experimental results on challenging data sets demonstrate the competitiveness of the proposed method comparing with several state-of-the-art interactive algorithms.

关键词： interactive image segmentation multilabel Markov random field (MRF) multilayer graph graph cuts parallel partial optimality

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ROBOT USERS FOR THE EVALUATION OF BOUNDARY-TRACKING APPROACHES IN interactive image segmentation

ROBOT USERS FOR THE EVALUATION OF BOUNDARY-TRACKING APPROACH...

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IEEE International Conference on image Processing

作者： Thiago V. Spina Alexandre X. Falcao Institute of Computing University of Campinas (UNICAMP)

ISBN: (纸本)9781479957521

Recent advances in interactive image segmentation focused on eliminating the user bias during evaluation by simulating their behavior using robot users. However, these robots only work for region-based methods, excluding the important class of approaches that rely on the boundary-tracking paradigm. We propose completely novel robot users that are able to simulate the human user behavior when segmenting an image through the addition of anchor points close to the object's boundary. Our robots constantly evaluate the optimum-boundary segment being computed from a previously selected anchor point to the current virtual mouse position, seeking for the longest possible segment with minimum acceptable error. A new anchor is added when the error is too high and the robots iterate until closing the contour, just like real users. We validate our robots by conducting a user study and extensive experiments, considering two boundary-tracking methods: live-wire-on-the-fly and Riverbed. We further show how our robot users can be used to assess hybrid approaches that combine boundary-tracking with region-based delineation, such as LiveMarkers, while conjecturing that robots might lead to new methods for automatic foreground segmentation.

关键词： Robot users interactive image segmentation Boundary-tracking Live wire Riverbed image Foresting Transform

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interactive color image segmentation with linear programming

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MACHINE VISION AND APPLICATIONS 2010年第4期21卷 403-412页

作者： Li, Hongdong Shen, Chunhua Australian Natl Univ Canberra ACT 0200 Australia NICTA Natl ICT Australia Canberra Res Lab Canberra ACT 2601 Australia

image segmentation is an important and fundamental task for image and vision understanding. This paper describes a linear programming (LP) approach for segmenting a color image into multiple regions. Compared with the recently proposed semi-definite programming (SDP)-based approach, our approach has a simpler mathematical formulation, and a far lower computational complexity. In particular, to segment an image of M x N pixels into k classes, our method requires only O((M N k) (m) ) complexity-a sharp contrast to the complexity of O((M N k)(2n) ) if the SDP method is adopted, where m and n are the polynomial complexity of the corresponding LP solver and SDP solver, respectively (in general we have ma parts per thousand(a) n). Such a significant reduction in computation readily enables our algorithm to process color images of reasonable sizes. For example, while the existing SDP relaxation algorithm is only able to segment a toy-size image of, e.g., 10 x 10 to 30 x 30 pixels in hours time, our algorithm can process larger color image of, say, 100 x 100 to 500 x 500 image in much shorter time.

关键词： interactive image segmentation Linear programming Object cutout

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interactive Medical image segmentation Using Deep Learning With image-Specific Fine Tuning

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2018年第7期37卷 1562-1573页

作者： Wang, Guotai Li, Wenqi Zuluaga, Maria A. Pratt, Rosalind Patel, Premal A. Aertsen, Michael Doel, Tom David, Anna L. Deprest, Jan Ourselin, Sebastien Vercauteren, Tom UCL Dept Med Phys & Biomed Engn Wellcome EPSRC Ctr Intervent & Surg Sci London WC1E 6BT England UCL Dept Med Phys & Biomed Engn London WC1E 6BT England Univ Nacl Colombia Fac Med Bogota 111321 Colombia Amadeus SAS F-06560 Sophia Antipolis France Univ Hosp KU Leuven Dept Radiol B-3000 Leuven Belgium UCL Inst Womens Hlth Wellcome EPSRC Ctr Intervent & Surg Sci London WC1E 6BT England Katholieke Univ Leuven Dept Obstet & Gynaecol B-3000 Leuven Belgium Katholieke Univ Leuven B-3000 Leuven Belgium

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address these problems, we propose a novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine tuning to make a CNN model adaptive to a specific test image, which can be either unsupervised(without additional user interactions) or supervised (with additional scribbles). We also propose a weighted loss function considering network and interaction-based uncertainty for the fine tuning. We applied this framework to two applications: 2-D segmentation of multiple organs from fetal magnetic resonance (MR) slices, where only two types of these organs were annotated for training and 3-D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema) from different MR sequences, where only the tumor core in one MR sequence was annotated for training. Experimental results show that: 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs;2) image-specific fine tuning with the proposed weighted loss function significantly improves segmentation accuracy;and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.

关键词： interactive image segmentation convolutional neural network fine-tuning fetal MRI brain tumor

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interactive CT image segmentation WITH ONLINE DISCRIMINATIVE LEARNING

INTERACTIVE CT IMAGE SEGMENTATION WITH ONLINE DISCRIMINATIVE...

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18th IEEE International Conference on image Processing (ICIP)

作者： Yang, Wei Wang, Xiaolong Lin, Liang Gao, Chengying Sun Yat Sen Univ Sch Software Guangzhou 510275 Guangdong Peoples R China

ISBN: (纸本)9781457713033

Although interactive image segmentation has been widely exploited, current approaches present unsatisfactory results in medical image processing. This paper proposes a fast method for interactive CT image segmentation in which the tumor regions should be partitioned as foreground against the healthy tissues. In contrast to natural images, we have the following observation on CT images: (1) CT images often include discontinuous silhouette or cluttered spots caused by input devices or patient corporeity;(2) Disease areas often have varying appearance and shape. We thus train a discriminative foreground/background model based on user-placed scribbles. In our method, we extract positive and negative samples according to the foreground and background scribbles respectively, and use dense SIFT descriptors plus gray-level histogram as candidate features. With online learning, segmentation can be fast solved by the Bregman iteration. We test our method on CT liver images and demonstrate the advantage by comparing to state-of-the-art approaches.

关键词： interactive image segmentation CT image online discriminative learning

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interactive Clothing image segmentation Based on Superpixels and Graph Cuts

Interactive Clothing Image Segmentation Based on Superpixels...

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International Conference on Computer Sciences and Applications (CSA)

作者： Li Wei-long Guangxi Univ Sci & Technol Sch Comp Liuzhou Peoples R China

ISBN: (纸本)9780769551258

In this paper, we propose an interactive clothing image segmentation method based on superpixels and Graph Cuts. Firstly, we process the image from pixels to superpixels with the method of SLIC to reduce the computational loads and lower the effect of noise, and then a graph is constructed using superpixels as nodes. Finally, min-cut/max-flow algorithm is applied to solve the energy function. In this process, the seeds of clothing or non-clothing are decided interactively. Some given samples show that the method can achieve promising clothing segmentation results.

关键词： interactive image segmentation SLIC Graph Cuts clothing segmentation

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Volumetric memory network for interactive medical image segmentation

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MEDICAL image ANALYSIS 2023年 83卷 102599页

作者： Zhou, Tianfei Li, Liulei Bredell, Gustav Li, Jianwu Unkelbach, Jan Konukoglu, Ender Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Univ Hosp Zurich Dept Radiat Oncol Zurich Switzerland

Despite recent progress of automatic medical image segmentation techniques, fully automatic results usually fail to meet clinically acceptable accuracy, thus typically require further refinement. To this end, we propose a novel Volumetric Memory Network, dubbed as VMN, to enable segmentation of 3D medical images in an interactive manner. Provided by user hints on an arbitrary slice, a 2D interaction network is firstly employed to produce an initial 2D segmentation for the chosen slice. Then, the VMN propagates the initial segmentation mask bidirectionally to all slices of the entire volume. Subsequent refinement based on additional user guidance on other slices can be incorporated in the same manner. To facilitate smooth human-in-the-loop segmentation, a quality assessment module is introduced to suggest the next slice for interaction based on the segmentation quality of each slice produced in the previous round. Our VMN demonstrates two distinctive features: First, the memory-augmented network design offers our model the ability to quickly encode past segmentation information, which will be retrieved later for the segmentation of other slices;Second, the quality assessment module enables the model to directly estimate the quality of each segmentation prediction, which allows for an active learning paradigm where users preferentially label the lowest-quality slice for multi-round refinement. The proposed network leads to a robust interactive segmentation engine, which can generalize well to various types of user annotations (e.g., scribble, bounding box, extreme clicking). Extensive experiments have been conducted on three public medical image segmentation datasets (i.e., MSD, KiTS19, CVC-ClinicDB), and the results clearly confirm the superiority of our approach in comparison with state-of-the-art segmentation models. The code is made publicly available at https://***/0liliulei/Mem3D.

关键词： interactive image segmentation Memory-augmented network Attention fully convolutional network Deep learning

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DeeplGeoS: A Deep interactive Geodesic Framework for Medical image segmentation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019年第7期41卷 1559-1572页

作者： Wang, Guotai Zuluaga, Maria A. Li, Wenqi Pratt, Rosalind Patel, Premal A. Aertsen, Michael Doel, Tom David, Anna L. Deprest, Jan Ourselin, Sebastien Vercauteren, Tom UCL Translat Imaging Grp Wellcome EPSRC Ctr Intervent & Surg Sci WEISS London WC1E 6BT England UCL Inst Womens Hlth London WC1E 6BT England Katholieke Univ Leuven Univ Hosp Dept Radiol B-3000 Leuven Belgium Katholieke Univ Leuven Dept Obstet Univ Hosp B-3000 Leuven Belgium

Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentations. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose to combine user interactions with CNNs through geodesic distance transforms, and propose a resolution-preserving network that gives a better dense prediction. In addition, we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field. We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.

关键词： interactive image segmentation convolutional neural network geodesic distance conditional random fields

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