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Jian M, Wu R, Xu W, Zhi H, Tao C, Chen H, Li X. VascuConNet: an enhanced connectivity network for vascular segmentation. Med Biol Eng Comput 2024; 62:3543-3554. [PMID: 38898202 DOI: 10.1007/s11517-024-03150-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 06/06/2024] [Indexed: 06/21/2024]
Abstract
Medical image segmentation commonly involves diverse tissue types and structures, including tasks such as blood vessel segmentation and nerve fiber bundle segmentation. Enhancing the continuity of segmentation outcomes represents a pivotal challenge in medical image segmentation, driven by the demands of clinical applications, focusing on disease localization and quantification. In this study, a novel segmentation model is specifically designed for retinal vessel segmentation, leveraging vessel orientation information, boundary constraints, and continuity constraints to improve segmentation accuracy. To achieve this, we cascade U-Net with a long-short-term memory network (LSTM). U-Net is characterized by a small number of parameters and high segmentation efficiency, while LSTM offers a parameter-sharing capability. Additionally, we introduce an orientation information enhancement module inserted into the model's bottom layer to obtain feature maps containing orientation information through an orientation convolution operator. Furthermore, we design a new hybrid loss function that consists of connectivity loss, boundary loss, and cross-entropy loss. Experimental results demonstrate that the model achieves excellent segmentation outcomes across three widely recognized retinal vessel segmentation datasets, CHASE_DB1, DRIVE, and ARIA.
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Affiliation(s)
- Muwei Jian
- School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China.
- School of Information Science and Technology, Linyi University, Linyi, China.
| | - Ronghua Wu
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Wenjin Xu
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Huixiang Zhi
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Chen Tao
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Hongyu Chen
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Xiaoguang Li
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China.
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Cai P, Li B, Sun G, Yang B, Wang X, Lv C, Yan J. DEAF-Net: Detail-Enhanced Attention Feature Fusion Network for Retinal Vessel Segmentation. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01207-6. [PMID: 39103564 DOI: 10.1007/s10278-024-01207-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 06/25/2024] [Accepted: 07/08/2024] [Indexed: 08/07/2024]
Abstract
Retinal vessel segmentation is crucial for the diagnosis of ophthalmic and cardiovascular diseases. However, retinal vessels are densely and irregularly distributed, with many capillaries blending into the background, and exhibit low contrast. Moreover, the encoder-decoder-based network for retinal vessel segmentation suffers from irreversible loss of detailed features due to multiple encoding and decoding, leading to incorrect segmentation of the vessels. Meanwhile, the single-dimensional attention mechanisms possess limitations, neglecting the importance of multidimensional features. To solve these issues, in this paper, we propose a detail-enhanced attention feature fusion network (DEAF-Net) for retinal vessel segmentation. First, the detail-enhanced residual block (DERB) module is proposed to strengthen the capacity for detailed representation, ensuring that intricate features are efficiently maintained during the segmentation of delicate vessels. Second, the multidimensional collaborative attention encoder (MCAE) module is proposed to optimize the extraction of multidimensional information. Then, the dynamic decoder (DYD) module is introduced to preserve spatial information during the decoding process and reduce the information loss caused by upsampling operations. Finally, the proposed detail-enhanced feature fusion (DEFF) module composed of DERB, MCAE and DYD modules fuses feature maps from both encoding and decoding and achieves effective aggregation of multi-scale contextual information. The experiments conducted on the datasets of DRIVE, CHASEDB1, and STARE, achieving Sen of 0.8305, 0.8784, and 0.8654, and AUC of 0.9886, 0.9913, and 0.9911 on DRIVE, CHASEDB1, and STARE, respectively, demonstrate the performance of our proposed network, particularly in the segmentation of fine retinal vessels.
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Affiliation(s)
- Pengfei Cai
- School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China
| | - Biyuan Li
- School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China.
- Tianjin Development Zone Jingnuohanhai Data Technology Co., Ltd, Tianjin, China.
| | - Gaowei Sun
- School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China
| | - Bo Yang
- School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China
| | - Xiuwei Wang
- School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China
| | - Chunjie Lv
- School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China
| | - Jun Yan
- School of Mathematics, Tianjin University, Tianjin, 300072, China
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Huang H, Shang Z, Yu C. FRD-Net: a full-resolution dilated convolution network for retinal vessel segmentation. BIOMEDICAL OPTICS EXPRESS 2024; 15:3344-3365. [PMID: 38855685 PMCID: PMC11161363 DOI: 10.1364/boe.522482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/13/2024] [Accepted: 04/17/2024] [Indexed: 06/11/2024]
Abstract
Accurate and automated retinal vessel segmentation is essential for performing diagnosis and surgical planning of retinal diseases. However, conventional U-shaped networks often suffer from segmentation errors when dealing with fine and low-contrast blood vessels due to the loss of continuous resolution in the encoding stage and the inability to recover the lost information in the decoding stage. To address this issue, this paper introduces an effective full-resolution retinal vessel segmentation network, namely FRD-Net, which consists of two core components: the backbone network and the multi-scale feature fusion module (MFFM). The backbone network achieves horizontal and vertical expansion through the interaction mechanism of multi-resolution dilated convolutions while preserving the complete image resolution. In the backbone network, the effective application of dilated convolutions with varying dilation rates, coupled with the utilization of dilated residual modules for integrating multi-scale feature maps from adjacent stages, facilitates continuous learning of multi-scale features to enhance high-level contextual information. Moreover, MFFM further enhances segmentation by fusing deeper multi-scale features with the original image, facilitating edge detail recovery for accurate vessel segmentation. In tests on multiple classical datasets,compared to state-of-the-art segmentation algorithms, FRD-Net achieves superior performance and generalization with fewer model parameters.
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Affiliation(s)
- Hua Huang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
| | - Zhenhong Shang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China
| | - Chunhui Yu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
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Li G, Xie J, Zhang L, Sun M, Li Z, Sun Y. MCAFNet: multiscale cross-layer attention fusion network for honeycomb lung lesion segmentation. Med Biol Eng Comput 2024; 62:1121-1137. [PMID: 38150110 DOI: 10.1007/s11517-023-02995-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/07/2023] [Indexed: 12/28/2023]
Abstract
Accurate segmentation of honeycomb lung lesions from lung CT images plays a crucial role in the diagnosis and treatment of various lung diseases. However, the availability of algorithms for automatic segmentation of honeycomb lung lesions remains limited. In this study, we propose a novel multi-scale cross-layer attention fusion network (MCAFNet) specifically designed for the segmentation of honeycomb lung lesions, taking into account their shape specificity and similarity to surrounding vascular shadows. The MCAFNet incorporates several key modules to enhance the segmentation performance. Firstly, a multiscale aggregation (MIA) module is introduced in the input part to preserve spatial information during downsampling. Secondly, a cross-layer attention fusion (CAF) module is proposed to capture multiscale features by integrating channel information and spatial information from different layers of the feature maps. Lastly, a bidirectional attention gate (BAG) module is constructed within the skip connection to enhance the model's ability to filter out background information and focus on the segmentation target. Experimental results demonstrate the effectiveness of the proposed MCAFNet. On the honeycomb lung segmentation dataset, the network achieves an Intersection over Union (IoU) of 0.895, mean IoU (mIoU) of 0.921, and mean Dice coefficient (mDice) of 0.949, outperforming existing medical image segmentation algorithms. Furthermore, experiments conducted on additional datasets confirm the generalizability and robustness of the proposed model. The contribution of this study lies in the development of the MCAFNet, which addresses the lack of automated segmentation algorithms for honeycomb lung lesions. The proposed network demonstrates superior performance in accurately segmenting honeycomb lung lesions, thereby facilitating the diagnosis and treatment of lung diseases. This work contributes to the existing literature by presenting a novel approach that effectively combines multi-scale features and attention mechanisms for lung lesion segmentation. The code is available at https://github.com/Oran9er/MCAFNet .
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Affiliation(s)
- Gang Li
- Taiyuan University of Technology Software College, Taiyuan, China
| | - Jinjie Xie
- Taiyuan University of Technology Software College, Taiyuan, China
| | - Ling Zhang
- Taiyuan University of Technology Software College, Taiyuan, China.
| | - Mengxia Sun
- Taiyuan University of Technology Software College, Taiyuan, China
| | - Zhichao Li
- Taiyuan University of Technology Software College, Taiyuan, China
| | - Yuanjin Sun
- Taiyuan University of Technology Software College, Taiyuan, China
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Wang Y, Yu X, Yang Y, Zhang X, Zhang Y, Zhang L, Feng R, Xue J. A multi-branched semantic segmentation network based on twisted information sharing pattern for medical images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107914. [PMID: 37992569 DOI: 10.1016/j.cmpb.2023.107914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/12/2023] [Accepted: 11/03/2023] [Indexed: 11/24/2023]
Abstract
BACKGROUND Semantic segmentation plays an indispensable role in clinical diagnosis support, intelligent surgical assistance, personalized treatment planning, and drug development, making it a core area of research in smart healthcare. However, the main challenge in medical image semantic segmentation lies in the accuracy bottleneck, primarily due to the low interactivity of feature information and the lack of deep exploration of local features during feature fusion. METHODS To address this issue, a novel approach called Twisted Information-sharing Pattern for Multi-branched Network (TP-MNet) has been proposed. This architecture facilitates the mutual transfer of features among neighboring branches at the next level, breaking the barrier of semantic isolation and achieving the goal of semantic fusion. Additionally, performing a secondary feature mining during the transfer process effectively enhances the detection accuracy. Building upon the Twisted Pattern transmission in the encoding and decoding stages, enhanced and refined modules for feature fusion have been developed. These modules aim to capture key features of lesions by acquiring contextual semantic information in a broader context. RESULTS The experiments extensively and objectively validated the TP-MNet on 5 medical datasets and compared it with 21 other semantic segmentation models using 7 metrics. Through metric analysis, image comparisons, process examination, and ablation tests, the superiority of TP-MNet was convincingly demonstrated. Additionally, further investigations were conducted to explore the limitations of TP-MNet, thereby clarifying the practical utility of the Twisted Information-sharing Pattern. CONCLUSIONS TP-MNet adopts the Twisted Information-sharing Pattern, leading to a substantial improvement in the semantic fusion effect and directly contributing to enhanced segmentation performance on medical images. Additionally, this semantic broadcasting mode not only underscores the importance of semantic fusion but also highlights a pivotal direction for the advancement of multi-branched architectures.
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Affiliation(s)
- Yuefei Wang
- College of Computer Science, Chengdu University, 2025 Chengluo Rd., Chengdu, Sichuan 610106, China
| | - Xi Yu
- Stirling College, Chengdu University, 2025 Chengluo Rd., Chengdu, Sichuan 610106, China.
| | - Yixi Yang
- Institute of Cancer Biology and Drug Discovery, Chengdu University, 2025 Chengluo Rd., Chengdu, Sichuan 610106, China
| | - Xiang Zhang
- College of Computer Science, Chengdu University, 2025 Chengluo Rd., Chengdu, Sichuan 610106, China
| | - Yutong Zhang
- College of Computer Science, Chengdu University, 2025 Chengluo Rd., Chengdu, Sichuan 610106, China
| | - Li Zhang
- College of Computer Science, Chengdu University, 2025 Chengluo Rd., Chengdu, Sichuan 610106, China
| | - Ronghui Feng
- Stirling College, Chengdu University, 2025 Chengluo Rd., Chengdu, Sichuan 610106, China
| | - Jiajing Xue
- Stirling College, Chengdu University, 2025 Chengluo Rd., Chengdu, Sichuan 610106, China
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Xi H, Dong H, Sheng Y, Cui H, Huang C, Li J, Zhu J. MSCT-UNET: multi-scale contrastive transformer within U-shaped network for medical image segmentation. Phys Med Biol 2023; 69:015022. [PMID: 38061069 DOI: 10.1088/1361-6560/ad135d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 12/07/2023] [Indexed: 12/30/2023]
Abstract
Objective.Automatic mutli-organ segmentation from anotomical images is essential in disease diagnosis and treatment planning. The U-shaped neural network with encoder-decoder has achieved great success in various segmentation tasks. However, a pure convolutional neural network (CNN) is not suitable for modeling long-range relations due to limited receptive fields, and a pure transformer is not good at capturing pixel-level features.Approach.We propose a new hybrid network named MSCT-UNET which fuses CNN features with transformer features at multi-scale and introduces multi-task contrastive learning to improve the segmentation performance. Specifically, the multi-scale low-level features extracted from CNN are further encoded through several transformers to build hierarchical global contexts. Then the cross fusion block fuses the low-level and high-level features in different directions. The deep-fused features are flowed back to the CNN and transformer branch for the next scale fusion. We introduce multi-task contrastive learning including a self-supervised global contrast learning and a supervised local contrast learning into MSCT-UNET. We also make the decoder stronger by using a transformer to better restore the segmentation map.Results.Evaluation results on ACDC, Synapase and BraTS datasets demonstrate the improved performance over other methods compared. Ablation study results prove the effectiveness of our major innovations.Significance.The hybrid encoder of MSCT-UNET can capture multi-scale long-range dependencies and fine-grained detail features at the same time. The cross fusion block can fuse these features deeply. The multi-task contrastive learning of MSCT-UNET can strengthen the representation ability of the encoder and jointly optimize the networks. The source code is publicly available at:https://github.com/msctunet/MSCT_UNET.git.
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Affiliation(s)
- Heran Xi
- School of Electronic Engineering, Heilongjiang University, Harbin, 150001, People's Republic of China
| | - Haoji Dong
- School of Computer Science and Technology, Heilongjiang University, Harbin, 150000, People's Republic of China
| | - Yue Sheng
- School of Computer Science and Technology, Heilongjiang University, Harbin, 150000, People's Republic of China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, 3000, Australia
| | - Chengying Huang
- School of Computer Science and Technology, Heilongjiang University, Harbin, 150000, People's Republic of China
| | - Jinbao Li
- Qilu University of Technology (Shandong Academy of Science), Shandong Artificial Intelligence Institute, Jinnan, 250014, People's Republic of China
| | - Jinghua Zhu
- School of Computer Science and Technology, Heilongjiang University, Harbin, 150000, People's Republic of China
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Wang K, Wang X, Xi Z, Li J, Zhang X, Wang R. Automatic Segmentation and Quantification of Abdominal Aortic Calcification in Lateral Lumbar Radiographs Based on Deep-Learning-Based Algorithms. Bioengineering (Basel) 2023; 10:1164. [PMID: 37892894 PMCID: PMC10604574 DOI: 10.3390/bioengineering10101164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/29/2023] Open
Abstract
To investigate the performance of deep-learning-based algorithms for the automatic segmentation and quantification of abdominal aortic calcification (AAC) in lateral lumbar radiographs, we retrospectively collected 1359 consecutive lateral lumbar radiographs. The data were randomly divided into model development and hold-out test datasets. The model development dataset was used to develop U-shaped fully convolutional network (U-Net) models to segment the landmarks of vertebrae T12-L5, the aorta, and anterior and posterior aortic calcifications. The AAC lengths were calculated, resulting in an automatic Kauppila score output. The vertebral levels, AAC scores, and AAC severity were obtained from clinical reports and analyzed by an experienced expert (reference standard) and the model. Compared with the reference standard, the U-Net model demonstrated a good performance in predicting the total AAC score in the hold-out test dataset, with a correlation coefficient of 0.97 (p <0.001). The overall accuracy for the AAC severity was 0.77 for the model and 0.74 for the clinical report. Additionally, the Kendall coefficient of concordance of the total AAC score prediction was 0.89 between the model-predicted score and the reference standard, and 0.88 between the structured clinical report and the reference standard. In conclusion, the U-Net-based deep learning approach demonstrated a relatively high model performance in automatically segmenting and quantifying ACC.
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Affiliation(s)
- Kexin Wang
- Department of Radiology, Peking University First Hospital, Beijing 100034, China
- School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing 100034, China
| | - Zuqiang Xi
- Beijing Smart Tree Medical Technology Co., Ltd., Beijing 102200, China
| | - Jialun Li
- Beijing Smart Tree Medical Technology Co., Ltd., Beijing 102200, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing 100034, China
| | - Rui Wang
- Department of Radiology, Peking University First Hospital, Beijing 100034, China
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