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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Gao, Yifan Li, Jun Chang, Xinyue Zhang, Yulong Chen, Riqing Yang, Changcai Wei, Yi Dong, Heng Wei, Lifang Fujian Agriculture and Forestry University College of Computer and Information Science Fujian China Nanjing University of Information Science and Technology Institute for Ai in Medicine School of Artificial Intelligence Nanjing China Fujian Medical University College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics Fujian China Fujian Agriculture and Forestry University Digital Fujian Research Institute of Big Data for Agriculture and Forestry Fujian China Wuyi University School of Mathematics and Computer Science Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Fujian China

ISBN: (纸本)9798350337488

multi-objective segmentation (MOS) in medical images is to simultaneously extract multiple regions of interest in the medical images. Due to the unbalanced distribution of samples and the similarity and significant differences between features in medical images, current methods still struggle to achieve satisfactory results. In this context, we propose a novel Morphological Guided Causal Constrain segmentation network (MCCSeg) for medical image multi-object segmentation. We introduced a Causal Constrain Module (CCM) for feature decorrelation by sample reweighting. The morphological guidance module (MG) is designed to extract the boundary features as the prior shape information for enhancing feature representation. Our experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, obtaining up 3.76% and 5.41% improvements in DICE and HD95 scores on Synapse dataset, respectively. © 2023 IEEE.

关键词： causal learning deep learning Medical image morphological guidance multi-object segmentation

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ODDF-Net: multi-object segmentation in 3D retinal OCTA using optical density and disease features

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KNOWLEDGE-BASED SYSTEMS 2024年 306卷

作者： Yang, Chaozhi Fan, Jiayue Bai, Yun Li, Yachuan Xiao, Qian Li, Zongmin Li, Hongyi Li, Hua China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China Chinese Acad Med Sci Beijing Hosp Inst Geriatr Med Beijing 100730 Peoples R China Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China

Automatic extraction of retinal structures, including Retinal Capillaries (RC), Retinal Arteries (RA), Retinal Veins (RV), and the Foveal Avascular Zone (FAZ), is crucial for the diagnosis and treatment of ocular diseases. This paper presents ODDF-Net, a segmentation network leveraging optical density and disease features, for the simultaneous 2D segmentation of RC, RA, RV, and FAZ in 3D Optical Coherence Tomography Angiography (OCTA). We introduce the concept of optical density to generate additional input images, enhancing the specificity for distinguishing arteries and veins. Our network employs a decoupled segmentation head to separate independent features of each object from shared features by focusing on object boundaries. Given the impact of ocular diseases on the morphology of retinal objects, we designed an auxiliary classification head and a cross-dimensional feature fusion module to model the relationship between various diseases and changes in retinal structures. Extensive experiments on two subsets of the OCTA-500 dataset demonstrate that ODDF-Net outperforms state-of-the-art methods, achieving mean intersection over union ratios of 88.17% and 82.80%. The source code is available at https://***/y8421036/ODDF-Net.

关键词： multi-object segmentation Retina OCTA Optical density 3D to 2D

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Optimal multi-object segmentation with novel gradient vector flow based shape priors

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2018年 69卷 96-111页

作者： Bai, Junjie Shah, Abhay Wu, Xiaodong Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA Univ Iowa Dept Radiat Oncol Iowa City IA 52242 USA

Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness. A major way to encode such a prior shape model is to use a mesh representation, which is prone to causing self-intersection or mesh folding. Those problems require complex and expensive algorithms to mitigate. In this paper, we propose a novel shape prior directly embedded in the voxel grid space, based on gradient vector flows of a pre-segmentation. The flexible and powerful prior shape representation is ready to be extended to simultaneously segmenting multiple interacting objects with minimum separation distance constraint. The segmentation problem of multiple interacting objects with shape priors is formulated as a Markov Random Field problem, which seeks to optimize the label assignment (objects or background) for each voxel while keeping the label consistency between the neighboring voxels. The optimization problem can be efficiently solved with a single minimum s-t cut in an appropriately constructed graph. The proposed algorithm has been validated on two multi-object segmentation applications: the brain tissue segmentation in MRI images and the bladder/prostate segmentation in CT images. Both sets of experiments showed superior or competitive performance of the proposed method to the compared state-of-the-art methods. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Shape priors Gradient vector flows multi-object segmentation segmentation

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On-line video multi-object segmentation based on skeleton model and occlusion detection

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multiMEDIA TOOLS AND APPLICATIONS 2018年第23期77卷 31313-31329页

作者： Huang, Guoheng Pun, Chi-Man Guangdong Univ Technol Sch Comp Guangzhou Guangdong Peoples R China Univ Macau Dept Comp & Informat Sci Taipa Macau Peoples R China

In this work, we propose an approach for on-line video multi object segmentation based on skeleton model and occlusion detection. We consider the multi-object segmentation in every frame as a multi-class region merging based object segmentation. We then generate the initial object superpixels automatically using a skeleton model from the second frame. Moreover, we also propose an initial background superpixel prediction scheme. In case the occlusion to affect the final segmentation result, we propose an occlusion detection model based on optical flow. The experimental results show that our method is both robust in segmenting multi objects and efficient in execution time.

关键词： multi-object segmentation Skeleton model Occlusion detection Superpixel On-line

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multi-object segmentation framework using deformable models for medical imaging analysis

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2016年第8期54卷 1181-1192页

作者： Namias, Rafael Pablo D'Amato, Juan del Fresno, Mariana Venere, Marcelo Pirro, Nicola Bellemare, Marc-Emmanuel UNR CONICET UAM France CIFASIS Blvd 27 Febrero 210 Bis Rosario Santa Fe Argentina Univ Nacl Ctr CONICET Tandil Argentina Univ Nacl Ctr Inst PLADEMA Tandil Argentina Univ Nacl Ctr CIC PBA Tandil Argentina Univ Nacl Ctr CNEA Tandil Argentina Hop La Timone Dept Digest Surg Marseille France Aix Marseille Univ LSIS UMR CNRS 7296 Marseille France

Segmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed fram

关键词： Deformable models segmentation framework multi-object segmentation Complex segmentation Collision control

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MallesNet: A multi-object assistance based network for brachial plexus segmentation in ultrasound images

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MEDICAL IMAGE ANALYSIS 2022年第0期80卷 102511页

作者： Ding, Yi Yang, Qiqi Wang, Yiqian Chen, Dajiang Qin, Zhiguang Zhang, Jian Univ Elect Sci & Technol China Network & Data Secur Key Lab Sichuan Prov Chengdu 610054 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Sichuan Peoples R China Ningbo WebKing Technol Joint Stock Co Ltd Ningbo 315000 Zhejiang Peoples R China Network & Data Secur Key Lab China Chengdu 610054 Sichuan Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Chengdu Med Coll Ctr Anaesthesia Surg Affilated Women & Childrens Hosp Sichuan Prov Hosp Women & Children Chengdu Peoples R China

Ultrasound-guided injection is widely used to help anesthesiologists perform anesthesia in peripheral nerve blockade (PNB). However, it is a daunting task to accurately identify nerve structure in ultrasound images even for the experienced anesthesiologists. In this paper, a multi-object assistance based Brachial Plexus segmentation Network, named MallesNet, is proposed to improve the nerve segmentation performance in ultrasound image with the assistance of simultaneously segmenting its surrounding anatomical structures (e.g., muscle, vein, and artery). The MallesNet is designed by following the framework of Mask R-CNN to implement the multi object identification and segmentation. Moreover, a spatial local contrast feature (SLCF) extraction module is proposed to compute contrast features at different scales to effectively obtain useful features for small objects. And the self-attention gate (SAG) is also utilized to capture the spatial relationships in different channels and further re-weight the channels in feature maps by following the design of non-local operation and channel attention. Furthermore, the upsampling mechanism in original Feature Pyramid Network (FPN) is improved by adopting the transpose convolution and skip concatenation to fine-tune the feature maps. The Ultrasound Brachial Plexus Dataset (UBPD) is also proposed to support the research on brachial plexus segmentation, which consists of 1055 ultrasound images with four objects (i.e., nerve, artery, vein and muscle) and their corresponding label masks. Extensive experimental results using UBPD dataset demonstrate that MallesNet can achieve a better segmentation performance on nerves structure and also on surrounding structures in comparison to other competing approaches. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Brachial plexus segmentation Ultrasound image Deep learning multi-object segmentation

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MCCSeg: Morphological embedding causal constraint network for medical image segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期249卷

作者： Gao, Yifan Wei, Lifang Li, Jun Chang, Xinyue Zhang, Yulong Chen, Riqing Yang, Changcai Wei, Yi Dong, Heng Fujian Agr & Forestry Univ Digital Fujian Res Inst Big Data Agr & Forestry Coll Comp & Informat Sci Fuzhou 350002 Peoples R China Nanjing Univ Informat Sci & Technol Inst AI Med Sch Artificial Intelligence Nanjing 210000 Peoples R China Fujian Med Univ Coll Clin Med Obstet & Gynecol & Pediat Dept Obstet & Gynecol Fuzhou 350001 Peoples R China Wuyi Univ Sch Math & Comp Sci Fujian Key Lab Big Data Applicat & Intellectualiza Wuyishan 354300 Fujian Peoples R China

Medical image multi-object segmentation aims to accurately extract each object that is great significant for the medical image analysis. Although several methods based on deep learning have been widely developed, it is still challenging due to the similarities of different objects, the significant morphological variability, data heterogeneity, and the unbalanced distribution of samples. To address the above issues, we propose a novel Morphological Embedding Causal Constraint segmentation network (MCCSeg) for medical image multi-object segmentation. We build a hybrid CNN-Transformer encoder module to obtain local and global features. Specifically, a Causal Constraint Module (CCM) is proposed to decorrelate features by sample reweighting, which utilizes weight allocation to train the weight of training samples. The Random Fourier Features (RFF) is employed to solve nonlinear dependency problems and remove correlations between features to mitigate the influence of spurious correlations. The morphological guidance module (MG) is designed to extract the boundary as prior morphological information for enhancing feature representation, which is concatenated into the deep supervision module in the decoder for further optimizing feature extraction. The experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, achieving improvements of 3.76% and 5.41% in terms of DICE and HD95 scores on Synapse dataset, respectively. The ablation experiments further verify the effectiveness of MCCSeg in improving generalization.

关键词： Medical image multi-object segmentation Deep learning Causal learning Morphological guidance

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Image segmentation by Hierarchical Layered Oriented Image Foresting Transform Subject to Closeness Constraints 3rd

Image Segmentation by Hierarchical Layered Oriented Image Fo...

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3rd International Joint Conference on Discrete Geometry and Mathematical Morphology (DGM)

作者： Dolabela Santos, Luiz Felipe de Souza Kleine, Felipe Augusto Vechiatto Miranda, Paulo Andre Univ Sao Paulo Inst Math & Stat Sao Paulo SP Brazil IPT Inst Technol Res State Sao Paulo Sao Paulo Brazil

ISBN: (纸本)9783031577925;9783031577932

In this work, we address the problem of image segmentation, subject to high-level constraints expected for the objects of interest. More specifically, we define closeness constraints to be used in conjunction with geometric constraints of inclusion in the Hierarchical Layered Oriented Image Foresting Transform (HLOIFT) algorithm. The proposed method can handle the segmentation of a hierarchy of objects with nested boundaries, each with its own expected boundary polarity constraint, making it possible to control the maximum distances (in a geodesic sense) between the successive nested boundaries. The method is demonstrated in the segmentation of nested objects in colored images with superior accuracy compared to its precursor methods and also when compared to some recent click-based methods.

关键词： multi-object segmentation Oriented Image Foresting Transform Layered graphs Closeness constraints

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A multi-objective segmentation method for chest X-rays based on collaborative learning from multiple partially annotated datasets

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INFORMATION FUSION 2024年 102卷

作者： Wang, Hongyu Zhang, Dandan Feng, Jun Cascone, Lucia Nappi, Michele Wan, Shaohua Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Xian Key Lab Big Data & Intelligent Comp Xian 710121 Shaanxi Peoples R China Northwest Univ Dept Informat Sci & Technol Xian 7101127 Shaanxi Peoples R China Univ Salerno Dept Comp Sci Salerno Italy Univ Elect Sci Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

Accurate segmentation of multiple targets, such as ribs, clavicles, heart, and lung fields, from chest X-ray images is crucial for diagnosing various lung diseases. Currently, mainstream deep learning methods heavily rely on fully annotated large-scale datasets. However, annotating all objects in chest X-ray images is a labor-intensive and time-consuming task. The publicly available partially annotated chest X-ray datasets have varying annotation objects and standards, and they are seldom utilized comprehensively in existing studies. To address these challenges, we propose A multi-objective segmentation Method for chest X-rays based on Collaborative Learning from multiple Partially Annotated Datasets (called: MSM-CLMPAD). Our approach first utilizes an encoder constructed with densely connected blocks to extract multi-scale features from multiple partially annotated datasets. Then, leveraging the overlapping relationships among segmentation targets, we design a dual decoder guided by the attention mechanism. The novel attention-guided decoder effectively disentangles the features corresponding to various targets. Importantly, we propose an alternating training strategy for different datasets, facilitating collaborative learning of the same network model across datasets with diverse annotation targets. The experimental results performing on four public datasets demonstrate that our method achieves superior Dice and Jaccard coefficients compared to other popular methods, particularly for overlapping targets and unclear regions. We also explore the mutual influence of different targets in chest X-rays, offering a solution for interaction among partial datasets and further alleviating the difficulties of data annotation for multi-organ segmentation tasks.

关键词： Chest X-rays multi-object segmentation Partially annotated data Medical image analysis

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Enhancing Organ at Risk segmentation with Improved Deep Neural Networks

Enhancing Organ at Risk Segmentation with Improved Deep Neur...

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Conference on Medical Imaging - Image Processing

作者： Isler, Ilkin S. Lisle, Curtis Rineer, Justin Kelly, Patrick Turgut, Damla Ricci, Jacob Bagci, Ulas Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA KnowledgeVis LLC Orlando FL USA Orlando Hlth Dept Radiat Oncol Orlando FL USA Northwestern Univ Dept Radiol & BME Chicago IL 60611 USA

ISBN: (数字)9781510649408

ISBN: (纸本)9781510649408;9781510649392

Organ at risk (OAR) segmentation is a crucial step for treatment planning and outcome determination in radiotherapy treatments of cancer patients. Several deep learning based segmentation algorithms have been developed in recent years, however, U-Net remains the de facto algorithm designed specifically for biomedical image segmentation and has spawned many variants with known weaknesses. In this study, our goal is to present simple architectural changes in U-Net to improve its accuracy and generalization properties. Unlike many other available studies evaluating their algorithms on single center data, we thoroughly evaluate several variations of U-Net as well as our proposed enhanced architecture on multiple data sets for an extensive and reliable study of the OAR segmentation problem. Our enhanced segmentation model includes (a)architectural changes in the loss function, (b)optimization framework, and (c)convolution type. Testing on three publicly available multi-object segmentation data sets, we achieved an average of 80% dice score compared to the baseline U-Net performance of 63%.

关键词： Organ at risk segmentation U-Net Generalization multi-object segmentation Radiation Oncology

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