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An evolutionary Chameleon Swarm Algorithm based network for 3d medical image segmentation

EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Rajesh, Chilukamari Sadam, Ravichandra Kumar, Sushil Natl Inst Technol Warangal Dept Comp Sci & Engn Warangal 506004 Telangana India Natl Inst Technol Kurukshetra Dept Comp Engn Kurukshetra 136119 Haryana India

developing 3d Convolutional Neural Networks (CNNs) for medical image segmentation is challenging due to the limited number of available labeled medical images and computational resources. Effective design of a 3d CNN requires a better structure and appropriate hyperparameters. Manually designing a neural network and choosing appropriate hyperparameters requires extensive knowledge, a time-consuming and challenging process, resulting in complex and over-parameterized networks. Neuroevolution is an automatic and effective approach to finding an optimal network structure and hyperparameters. This paper proposes an evolutionary based method using Chameleon Swarm Algorithm (CSA) to develop 3d CNN networks for volume segmentation. We also design a search space to build optimal 3d block-based encoder-decoder structure networks for medical image segmentation. We evaluated the proposed method on two different datasets (CT Spleen and MRI Heart) and compared the results of the obtained model with state-of-the-art models. Furthermore, the comparative results show that our proposed method yields 3d CNN networks with superior performance and fewer parameters than other state-of-the-art models utilized in 3d medical image segmentation.

关键词： Neural architecture search 3d medical image segmentation Chameleon swarm algorithm

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Hybrid Masked image Modeling for 3d medical image segmentation

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IEEE JOURNAL OF BIOmedical ANd HEALTH INFORMATICS 2024年第4期28卷 2115-2125页

作者： Xing, Zhaohu Zhu, Lei Yu, Lequan Xing, Zhiheng Wan, Liang Tianjin Univ Med Sch Tianjin 300070 Peoples R China Hong Kong Univ Sci & Technol Guangzhou 510530 Peoples R China Hong Kong Univ Sci & Technol Henan Key Lab Imaging & Intelligent Proc Guangzhou 510530 Peoples R China Univ Hong Kong Hong Kong Peoples R China Tianjin Univ Haihe Hosp Tianjin 300072 Peoples R China Tianjin Univ Coll Comp & Intelligence Tianjin Peoples R China

Masked image modeling (MIM) with transformer backbones has recently been exploited as a powerful self-supervised pre-training technique. The existing MIM methods adopt the strategy to mask random patches of the image and reconstruct the missing pixels, which only considers semantic information at a lower level, and causes a long pre-training time. This paper presents HybridMIM, a novel hybrid self-supervised learning method based on masked image modeling for 3d medical image segmentation. Specifically, we design a two-level masking hierarchy to specify which and how patches in sub-volumes are masked, effectively providing the constraints of higher level semantic information. Then we learn the semantic information of medical images at three levels, including: 1) partial region prediction to reconstruct key contents of the 3d image, which largely reduces the pre-training time burden (pixel-level);2) patch-masking perception to learn the spatial relationship between the patches in each sub-volume (region-level);and 3) drop-out-based contrastive learning between samples within a mini-batch, which further improves the generalization ability of the framework (sample-level). The proposed framework is versatile to support both CNN and transformer as encoder backbones, and also enables to pre-train decoders for image segmentation. We conduct comprehensive experiments on five widely-used public medical image segmentation datasets, including BraTS2020, BTCV, MSd Liver, MSd Spleen, and BraTS2023. The experimental results show the clear superiority of HybridMIM against competing supervised methods, masked pre-training approaches, and other self-supervised methods, in terms of quantitative metrics, speed performance and qualitative observations.

关键词： Masked image modeling 3d medical image segmentation Self-supervised learning

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Hybrid 3d medical image segmentation Using CNN and Frequency Transformer Fusion

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ARABIAN JOURNAL FOR SCIENCE ANd ENGINEERING 2024年 1-14页

作者： Labbihi, Ismayl Meslouhi, Othmane El Elassad, Zouhair Elamrani Abou Benaddy, Mohamed Kardouchi, Mustapha Akhloufi, Moulay Ibn Zohr Univ Fac Sci LabSI Lab Agadir 80000 Morocco Cadi Ayyad Univ Natl Sch Appl Sci SARS Grp Safi 46000 Morocco Univ Moncton Dept Comp Sci PRIME Lab 18 Antonine Maillet Ave Moncton NB E1A 3E9 Canada

medical image segmentation poses a significant challenge, particularly with 3d images. This study presents a novel hybrid network for 3d medical image segmentation, which sequentially integrates convolutional neural networks (CNNs) and transformer models. Structured with an encoder-decoder architecture, the network comprises a frequency transformer branch and a variational autoencoder branch. Initially, the CNN encoder extracts the features of volumetric spatial from the input 3d image. These features are subsequently converted into vectors and incorporated into a frequency transformer. This transformer employs fast Fourier transforms instead of self-attention to mitigate computational complexity. Significantly, this research marks the inaugural utilization of the Fourier transform on transformers for image segmentation employing 3d datasets. The network's efficacy was evaluated on kidney, tumor, and brain 3d datasets. It achieved remarkable results, such as a 97.54% dice score and a 95%-Hausdorff distance (95%-Hd) of 3.26 mm for kidney segmentation, and an 86.92% dice score with a 95%-Hd of 2.57 mm for brain tumor segmentation. These results underscore the network's potential for accurate and reliable segmentation in diverse medical imaging applications.

关键词： 3d medical image segmentation Convolutional neural networks Frequency transformer Variational autoencoder Fourier transforms

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Hybrid transformer-CNN with boundary-awareness network for 3d medical image segmentation

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APPLIEd INTELLIGENCE 2023年第23期53卷 28542-28554页

作者： He, Jianfei Xu, Canhui Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266000 Shandong Peoples R China

3d volumetric medical image segmentation is a crucial task in computer-aided diagnosis applications, but it remains challenging due to low contrast and boundary ambiguity between organs and surrounding tissues. Considering that accurate boundary voxels are of importance for organ segmentation, which relies on rich detailed features information. The most recent convolutional neural networks and transformer networks have attempted to enhance 2d boundaries during feature extraction. Few approaches focus on boundary voxels preservation for 3d scenarios. To address these issues, we propose the Hybrid Transformer-CNN with Boundary-awareness(HTCB-Net) network, which follows an encoder-decoder segmentation paradigm with learnable boundary modules. The 3d swin-transformer encoder is embedded with auxiliary object-related boundary map by designing a learnable boundary extracting module(BEM), which assists model in obtaining rich and discriminative feature representations. Our boundary map obtained from BEM supervises explicitly feature extraction process. Subsequently, boundary preserving module(BPM) adopts a novel fusion strategy, which integrates extracted boundary map and the corresponding encoder features. Through this module, the boundary position awareness is combined for feature enhancement with spatial complement and channel attention. We evaluate the performance of the proposed method with quantitative experiments on three public available datasets in both CT and MRI modalities: OAI-ZIB, Spleen, Pancreas. The comparative experimental results demonstrate that our HTCB-Net preserves more precise 3d boundaries and obtains significant improvements, particularly in terms of Average Symmetric Surface distance(ASSd).

关键词： 3d medical image segmentation Transformer Boundary preserving

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Unsupervised Cross-Modality Adaptation via dual Structural-Oriented Guidance for 3d medical image segmentation

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IEEE TRANSACTIONS ON medical IMAGING 2023年第6期42卷 1774-1785页

作者： Xian, Junlin Li, Xiang Tu, dandan Zhu, Senhua Zhang, Changzheng Liu, Xiaowu Li, Xin Yang, Xin Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Huawei Technol Co Ltd Cloud BU Shenzhen 511464 Peoples R China Huazhong Univ Sci & Technol Union Hosp Tongji Med Coll Dept Radiol Wuhan 430074 Peoples R China Hubei Prov Key Lab Mol Imaging Wuhan 430022 Peoples R China

deep convolutional neural networks (CNNs) have achieved impressive performance in medical image segmentation;however, their performance could degrade significantly when being deployed to unseen data with heterogeneous characteristics. Unsupervised domain adaptation (UdA) is a promising solution to tackle this problem. In this work, we present a novel UdA method, named dual adaptation-guiding network (dAG-Net), which incorporates two highly effective and complementary structural-oriented guidance in training to collaboratively adapt a segmentation model from a labelled source domain to an unlabeled target domain. Specifically, our dAG-Net consists of two core modules: 1) Fourier-based contrastive style augmentation (FCSA) which implicitly guides the segmentation network to focus on learning modality-insensitive and structural-relevant features, and 2) residual space alignment (RSA) which provides explicit guidance to enhance the geometric continuity of the prediction in the target modality based on a 3d prior of inter-slice correlation. We have extensively evaluated our method with cardiac substructure and abdominal multi-organ segmentation for bidirectional cross-modality adaptation between MRI and CT images. Experimental results on two different tasks demonstrate that our dAG-Net greatly outperforms the state-of-the-art UdA approaches for 3d medical image segmentation on unlabeled target images.

关键词： image segmentation Biomedical imaging Three-dimensional displays Feature extraction Training Magnetic resonance imaging Computed tomography Unsupervised domain adaptation 3d medical image segmentation structural-oriented guidance

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Adaptive decomposition and Shared Weight Volumetric Transformer Blocks for Efficient Patch-Free 3d medical image segmentation

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IEEE JOURNAL OF BIOmedical ANd HEALTH INFORMATICS 2023年第10期27卷 4854-4865页

作者： Wang, Hongyi Xu, Yingying Chen, Qingqing Tong, Ruofeng Chen, Yen-Wei Hu, Hongjie Lin, Lanfen Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310063 Peoples R China Zhejiang Lab Res Ctr Healthcare Data Sci Hangzhou 311121 Peoples R China Sir Run Run Shaw Hosp Dept Radiol Hangzhou 310016 Peoples R China Ritsumeikan Univ Coll Informat Sci & Engn Kusatsu 5250058 Japan

High resolution (HR) 3d medical image segmentation is vital for an accurate diagnosis. However, in the field of medical imaging, it is still a challenging task to achieve a high segmentation performance with cost-effective and feasible computation resources. Previous methods commonly use patch-sampling to reduce the input size, but this inevitably harms the global context and decreases the model's performance. In recent years, a few patch-free strategies have been presented to deal with this issue, but either they have limited performance due to their over-simplified model structures or they follow a complicated training process. In this study, to effectively address these issues, we present Adaptive decomposition (A-decomp) and Shared Weight Volumetric Transformer Blocks (SW-VTB). A-decomp can adaptively decompose features and reduce their spatial size, which greatly lowers GPU memory consumption. SW-VTB is able to capture long-range dependencies at a low cost with its lightweight design and cross-scale weight-sharing mechanism. Our proposed cross-scale weight-sharing approach enhances the network's ability to capture scale-invariant core semantic information in addition to reducing parameter numbers. By combining these two designs together, we present a novel patch-free segmentation framework named VolumeFormer. Experimental results on two datasets show that VolumeFormer outperforms existing patch-based and patch-free methods with a comparatively fast inference speed and relatively compact design.

关键词： 3d medical image segmentation deep learning patch-free vision transformer

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Semi-Supervised 3d medical image segmentation Based on dual-Task Consistent Joint Learning and Task-Level Regularization

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY ANd BIOINFORMATICS 2023年第4期20卷 2457-2467页

作者： Chen, Qi-Qi Sun, Zhao-Hui Wei, Chuan-Feng Wu, Edmond Q. Ming, dong Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China CAST Inst Manned Space Syst Engn Human Space Flight Syst Engn Div Beijing 100094 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China Natl Aeronaut Radio Elect Res Inst Sci & Technol Av Integrat Lab Shanghai 200240 Peoples R China Tianjin Univ Acad Med Engn & Translat Med Tianjin 300072 Peoples R China Tianjin Univ Coll Precis Instruments & Optoelect Engn Tianjin 300072 Peoples R China

Semi-supervised learning has attracted wide attention from many researchers since its ability to utilize a few data with labels and relatively more data without labels to learn information. Some existing semi-supervised methods for medical image segmentation enforce the regularization of training by implicitly perturbing data or networks to perform the consistency. Most consistency regularization methods focus on data level or network structure level, and rarely of them focus on the task level. It may not directly lead to an improvement in task accuracy. To overcome the problem, this work proposes a semi-supervised dual-task consistent joint learning framework with task-level regularization for 3d medical image segmentation. Two branches are utilized to simultaneously predict the segmented and signed distance maps, and they can learn useful information from each other by constructing a consistency loss function between the two tasks. The segmentation branch learns rich information from both labeled and unlabeled data to strengthen the constraints on the geometric structure of the target. Experimental results on two benchmark datasets show that the proposed method can achieve better performance compared with other state-of-the-art works. It illustrates our method improves segmentation performance by utilizing unlabeled data and consistent regularization.

关键词： Semi-supervised learning 3d medical image segmentation dual-task joint consistency loss

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3d medical image segmentation using parallel transformers

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PATTERN RECOGNITION 2023年第1期138卷

作者： Yan, Qingsen Liu, Shengqiang Xu, Songhua dong, Caixia Li, Zongfang Shi, Javen Qinfeng Zhang, Yanning dai, duwei Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Xi An Jiao Tong Univ Affiliated Hosp 2 Inst Med Artificial Intelligence Xian 710004 Peoples R China Univ Adelaide Australian Inst Machine Learning Adelaide 5005 Australia

Most recent 3d medical image segmentation methods adopt convolutional neural networks (CNNs) that rely on deep feature representation and achieve adequate performance. However, due to the convolutional architectures having limited receptive fields, they cannot explicitly model the long-range dependencies in the medical image. Recently, Transformer can benefit from global dependencies using self-attention mechanisms and learn highly expressive representations. Some works were designed based on the Trans-formers, but the existing Transformers suffer from extreme computational and memories, and they can-not take full advantage of the powerful feature representations in 3d medical image segmentation. In this paper, we aim to connect the different resolution streams in parallel and propose a novel network, named Transformer based High Resolution Network (TransHRNet), with an Effective Transformer (EffTrans) block, which has sufficient feature representation even at high feature resolutions . Given a 3d image, the encoder first utilizes CNN to extract the feature representations to capture the local information, and then the different f eature maps are reshaped elaborately for tokens that are fed into each Transformer stream in parallel to learn the global information and repeatedly exchange the information across streams. Unfortu-nately, the proposed framework based on the standard Transformer needs a huge amount of computation, thus we introduce a deep and effective Transformer to deliver better performance with fewer parameters. The proposed TransHRNet is evaluated on the Multi-Atlas Labeling Beyond the Cranial Vault (BCV) dataset that consists of 11 major human organs and the medical segmentation decathlon (MSd) dataset for brain tumor and spleen segmentation tasks. Experimental results show that it performs better than the convo-lutional and other related Transformer-based methods on the 3d multi-organ segmentation tasks. Code is available at https://***/duwei

关键词： 3d medical image segmentation deep learning Transformers Attention Fusion High -resolution representations Low -resolution representations

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Target area distillation and section attention segmentation network for accurate 3d medical image segmentation

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HEALTH INFORMATION SCIENCE ANd SYSTEMS 2023年第1期11卷 9页

作者： Xie, Ruiwei Pan, dan Zeng, An Xu, Xiaowei Wang, Tianchen Ullah, Najeeb Ji, Yuzhu Guangdong Univ Technol Guangzhou Guangdong Peoples R China Guangdong Polytech Normal Univ Guangzhou Guangdong Peoples R China Guangdong Prov Peoples Hosp Guangzhou Guangdong Peoples R China Mardan Univ Engn & Technol Mardan Pakistan

3d medical image segmentation has an essential role in medical image analysis, while attention mechanism has improved the performance by a large margin. However, existing methods obtained the attention coefficient in a small receptive field, resulting in possible performance limitations. Radiologists usually scan all the slices first to have an overall idea of the target, and then analyze regions of interest in multiple 2d views in clinic practice. We simulate radiologists' recognition process and propose to exploit the 3d context information in a deeper manner for accurate 3d medical images segmentation. due to the similarity of human body structure, medical images of different populations have highly similar shape and location information, so we use target region distillation to extract the common segmented region information. Particularly, we proposed two optimizations including Target Area distillation and Section Attention. Target Area distillation adds positions information to the original input to let the network has an initial attention of the target, while section attention performs attention extraction in three 2d sections thus with large range of receptive field. We compare our method against several popular networks in two public datasets including imageCHd and COVId-19. Experimental results show that our proposed method improves the segmentation dice score by 2-4% over the state-of-the-art methods. Our code has been released to the public (Anonymous link).

关键词： 3d medical image segmentation Section attention Target area distillation Transformer U-Net

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dUAL SELF-dISTILLATION OF U-SHAPEd NETWORKS FOR 3d medical image segmentation 21

DUAL SELF-DISTILLATION OF U-SHAPED NETWORKS FOR 3D MEDICAL I...

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21st IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Banerjee, Soumyanil dong, Ming Glide-Hurst, Carri Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Univ Wisconsin Dept Human Oncol Madison WI USA

ISBN: (纸本)9798350313345;9798350313338

U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation. In this paper, we propose a novel dual self-distillation (dSd) framework for U-shaped networks for 3d medical image segmentation. dSd distills knowledge from the ground-truth segmentation labels to the decoder layers and also between the encoder and decoder layers of a single U-shaped network. dSd is a generalized training strategy that could be attached to the backbone architecture of any U-shaped network to further improve its segmentation performance. We attached dSd on two state-of-the-art U-shaped backbones, and extensive experiments on two public 3d medical image segmentation datasets demonstrated significant improvement over those backbones, with negligible increase in trainable parameters and training time. The source code is publicly available at https://***/soumbane/dualSelfdistillation.

关键词： 3d medical image segmentation dual self-distillation U-shaped networks

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