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陈 英, 张 伟, 林 洪, 郑 铖, 周 滔, 冯 龙, 易 珍, 刘 岚. [A survey of loss function of medical image segmentation algorithms]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:392-400. [PMID: 37139774 PMCID: PMC10162910 DOI: 10.7507/1001-5515.202206038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 12/14/2022] [Indexed: 05/05/2023]
Abstract
Medical image segmentation based on deep learning has become a powerful tool in the field of medical image processing. Due to the special nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positive, false negative, etc. In view of these problems, researchers mostly improve the network structure, but rarely improve from the unstructured aspect. The loss function is an important part of the segmentation method based on deep learning. The improvement of the loss function can improve the segmentation effect of the network from the root, and the loss function is independent of the network structure, which can be used in various network models and segmentation tasks in plug and play. Starting from the difficulties in medical image segmentation, this paper first introduces the loss function and improvement strategies to solve the problems of sample imbalance, edge blur, false positive and false negative. Then the difficulties encountered in the improvement of the current loss function are analyzed. Finally, the future research directions are prospected. This paper provides a reference for the reasonable selection, improvement or innovation of loss function, and guides the direction for the follow-up research of loss function.
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Affiliation(s)
- 英 陈
- 南昌航空大学 软件学院(南昌 330063)School of Software, Nanchang Hangkong University , Nanchang 330063, P. R. China
| | - 伟 张
- 南昌航空大学 软件学院(南昌 330063)School of Software, Nanchang Hangkong University , Nanchang 330063, P. R. China
| | - 洪平 林
- 南昌航空大学 软件学院(南昌 330063)School of Software, Nanchang Hangkong University , Nanchang 330063, P. R. China
| | - 铖 郑
- 南昌航空大学 软件学院(南昌 330063)School of Software, Nanchang Hangkong University , Nanchang 330063, P. R. China
| | - 滔辉 周
- 南昌航空大学 软件学院(南昌 330063)School of Software, Nanchang Hangkong University , Nanchang 330063, P. R. China
| | - 龙锋 冯
- 南昌航空大学 软件学院(南昌 330063)School of Software, Nanchang Hangkong University , Nanchang 330063, P. R. China
| | - 珍 易
- 南昌航空大学 软件学院(南昌 330063)School of Software, Nanchang Hangkong University , Nanchang 330063, P. R. China
| | - 岚 刘
- 南昌航空大学 软件学院(南昌 330063)School of Software, Nanchang Hangkong University , Nanchang 330063, P. R. China
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孙 玉, 刘 嘉, 孙 泽, 韩 建, 于 宁. [A generative adversarial network-based unsupervised domain adaptation method for magnetic resonance image segmentation]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2022; 39:1181-1188. [PMID: 36575088 PMCID: PMC9927195 DOI: 10.7507/1001-5515.202203009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 10/23/2022] [Indexed: 12/29/2022]
Abstract
Intelligent medical image segmentation methods have been rapidly developed and applied, while a significant challenge is domain shift. That is, the segmentation performance degrades due to distribution differences between the source domain and the target domain. This paper proposed an unsupervised end-to-end domain adaptation medical image segmentation method based on the generative adversarial network (GAN). A network training and adjustment model was designed, including segmentation and discriminant networks. In the segmentation network, the residual module was used as the basic module to increase feature reusability and reduce model optimization difficulty. Further, it learned cross-domain features at the image feature level with the help of the discriminant network and a combination of segmentation loss with adversarial loss. The discriminant network took the convolutional neural network and used the labels from the source domain, to distinguish whether the segmentation result of the generated network is from the source domain or the target domain. The whole training process was unsupervised. The proposed method was tested with experiments on a public dataset of knee magnetic resonance (MR) images and the clinical dataset from our cooperative hospital. With our method, the mean Dice similarity coefficient (DSC) of segmentation results increased by 2.52% and 6.10% to the classical feature level and image level domain adaptive method. The proposed method effectively improves the domain adaptive ability of the segmentation method, significantly improves the segmentation accuracy of the tibia and femur, and can better solve the domain transfer problem in MR image segmentation.
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Affiliation(s)
- 玉波 孙
- 南开大学 人工智能学院(天津 300350)College of Artificial Intelligence, Nankai University, Tianjin 300350, P. R. China
- 南开大学 天津市智能机器人技术重点实验室(天津 300350)Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, P. R. China
| | - 嘉男 刘
- 南开大学 人工智能学院(天津 300350)College of Artificial Intelligence, Nankai University, Tianjin 300350, P. R. China
- 南开大学 天津市智能机器人技术重点实验室(天津 300350)Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, P. R. China
| | - 泽文 孙
- 南开大学 人工智能学院(天津 300350)College of Artificial Intelligence, Nankai University, Tianjin 300350, P. R. China
| | - 建达 韩
- 南开大学 人工智能学院(天津 300350)College of Artificial Intelligence, Nankai University, Tianjin 300350, P. R. China
- 南开大学 天津市智能机器人技术重点实验室(天津 300350)Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, P. R. China
- 北京大学第三医院 运动医学研究所(北京 100083)Institute of Sports Medicine, Peking University Third Hospital, Beijing 100083, P. R. China
| | - 宁波 于
- 南开大学 人工智能学院(天津 300350)College of Artificial Intelligence, Nankai University, Tianjin 300350, P. R. China
- 南开大学 天津市智能机器人技术重点实验室(天津 300350)Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, P. R. China
- 北京大学第三医院 运动医学研究所(北京 100083)Institute of Sports Medicine, Peking University Third Hospital, Beijing 100083, P. R. China
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吴 玉, 林 岚, 吴 水. [Multimodal high-grade glioma semantic segmentation network with multi-scale and multi-attention fusion mechanism]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2022; 39:433-440. [PMID: 35788512 PMCID: PMC10950780 DOI: 10.7507/1001-5515.202103021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/25/2022] [Indexed: 06/15/2023]
Abstract
Glioma is a primary brain tumor with high incidence rate. High-grade gliomas (HGG) are those with the highest degree of malignancy and the lowest degree of survival. Surgical resection and postoperative adjuvant chemoradiotherapy are often used in clinical treatment, so accurate segmentation of tumor-related areas is of great significance for the treatment of patients. In order to improve the segmentation accuracy of HGG, this paper proposes a multi-modal glioma semantic segmentation network with multi-scale feature extraction and multi-attention fusion mechanism. The main contributions are, (1) Multi-scale residual structures were used to extract features from multi-modal gliomas magnetic resonance imaging (MRI); (2) Two types of attention modules were used for features aggregating in channel and spatial; (3) In order to improve the segmentation performance of the whole network, the branch classifier was constructed using ensemble learning strategy to adjust and correct the classification results of the backbone classifier. The experimental results showed that the Dice coefficient values of the proposed segmentation method in this article were 0.909 7, 0.877 3 and 0.839 6 for whole tumor, tumor core and enhanced tumor respectively, and the segmentation results had good boundary continuity in the three-dimensional direction. Therefore, the proposed semantic segmentation network has good segmentation performance for high-grade gliomas lesions.
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Affiliation(s)
- 玉超 吴
- 北京工业大学 环境与生命科学学院 生物医学工程系 智能化生理测量与临床转化北京市国际科研合作基地(北京 100124)Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing 100124, P. R. China
| | - 岚 林
- 北京工业大学 环境与生命科学学院 生物医学工程系 智能化生理测量与临床转化北京市国际科研合作基地(北京 100124)Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing 100124, P. R. China
| | - 水才 吴
- 北京工业大学 环境与生命科学学院 生物医学工程系 智能化生理测量与临床转化北京市国际科研合作基地(北京 100124)Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing 100124, P. R. China
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Application of CNN Algorithm Based on Chaotic Recursive Diagonal Model in Medical Image Processing. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:6168562. [PMID: 34539771 PMCID: PMC8445709 DOI: 10.1155/2021/6168562] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/30/2021] [Accepted: 09/01/2021] [Indexed: 11/17/2022]
Abstract
With the gradual improvement of people's living standards, the production and drinking of all kinds of food is increasing. People's disease rate has increased compared with before, which leads to the increasing number of medical image processing. Traditional technology cannot meet most of the needs of medicine. At present, convolutional neural network (CNN) algorithm using chaotic recursive diagonal model has great advantages in medical image processing and has become an indispensable part of most hospitals. This paper briefly introduces the use of medical science and technology in recent years. The hybrid algorithm of CNN in chaotic recursive diagonal model is mainly used for technical research, and the application of this technology in medical image processing is analysed. The CNN algorithm is optimized by using chaotic recursive diagonal model. The results show that the chaotic recursive diagonal model can improve the structure of traditional neural network and improve the efficiency and accuracy of the original CNN algorithm. Then, the application research and comparison of medical image processing are performed according to CNN algorithm and optimized CNN algorithm. The experimental results show that the CNN algorithm optimized by chaotic recursive diagonal model can help medical image automatic processing and patient condition analysis.
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Ding J, Lin Q, Zhang J, Young GM, Jiang C, Zhong Y, Zhang J. Rapid identification of pathogens by using surface-enhanced Raman spectroscopy and multi-scale convolutional neural network. Anal Bioanal Chem 2021; 413:3801-3811. [PMID: 33961103 DOI: 10.1007/s00216-021-03332-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/30/2021] [Accepted: 04/08/2021] [Indexed: 12/17/2022]
Abstract
Salmonella is a prevalent pathogen causing serious morbidity and mortality worldwide. There are over 2600 serovars of Salmonella. Among them, Salmonella Enteritidis, Salmonella Typhimurium, and Salmonella Paratyphi were reported to be the most common foodborne pathogenic serovars in the EU and China. In order to provide a more efficient approach to detect and distinguish these serovars, a new analytical method was developed by combining surface-enhanced Raman spectroscopy (SERS) with multi-scale convolutional neural network (CNN). We prepared 34-nm gold nanoparticles (AuNPs) as the label-free Raman substrate, measured 1854 SERS spectra of these three Salmonella serovars, and then proposed a multi-scale CNN model with three parallel CNNs to achieve multi-dimensional extraction of SERS spectral features. We observed the impact of the number of iterations and training samples on the recognition accuracy by changing the ratio of the number of the training and testing sets. By comparing the calculated data with experimental one, it was shown that our model could reach recognition accuracy more than 97%. These results indicate that it was not only feasible to combine SERS spectroscopy with multi-scale CNN for Salmonella serotype identification, but also for other pathogen species and serovar identifications.
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Affiliation(s)
- Jingyu Ding
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China
| | - Qingqing Lin
- Key Laboratory of Ministry of Education of China for Research of Design and Electromagnetic Compatibility of High-Speed Electronic System, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jiameng Zhang
- Key Laboratory of Ministry of Education of China for Research of Design and Electromagnetic Compatibility of High-Speed Electronic System, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Glenn M Young
- Department of Food Science and Technology, University of California, Davis, CA, 95616, USA
| | - Chun Jiang
- Key Laboratory of Ministry of Education of China for Research of Design and Electromagnetic Compatibility of High-Speed Electronic System, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yaoguang Zhong
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China.
| | - Jianhua Zhang
- School of Agriculture and Biology, Bor S. Luh Food Safety Research Center, Shanghai Jiao Tong University, Shanghai, 200240, China.
- NMPA Key Laboratory for Testing Technology of Pharmaceutical Microbiology, Shanghai Institute for Food and Drug Control, Shanghai, 201203, China.
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