Lei Chai , Shuangqian Xue , Daodao Tang , Jixin Liu , Ning Sun , Xiujuan Liu
{"title":"TLF:从不可靠的标记 CTA 扫描中分割颅内动脉瘤的三重学习框架","authors":"Lei Chai , Shuangqian Xue , Daodao Tang , Jixin Liu , Ning Sun , Xiujuan Liu","doi":"10.1016/j.compmedimag.2024.102421","DOIUrl":null,"url":null,"abstract":"<div><p>Intracranial aneurysm (IA) is a prevalent disease that poses a significant threat to human health. The use of computed tomography angiography (CTA) as a diagnostic tool for IAs remains time-consuming and challenging. Deep neural networks (DNNs) have made significant advancements in the field of medical image segmentation. Nevertheless, training large-scale DNNs demands substantial quantities of high-quality labeled data, making the annotation of numerous brain CTA scans a challenging endeavor. To address these challenges and effectively develop a robust IAs segmentation model from a large amount of unlabeled training data, we propose a triple learning framework (TLF). The framework primarily consists of three learning paradigms: pseudo-supervised learning, contrastive learning, and confident learning. This paper introduces an enhanced mean teacher model and voxel-selective strategy to conduct pseudo-supervised learning on unreliable labeled training data. Concurrently, we construct the positive and negative training pairs within the high-level semantic feature space to improve the overall learning efficiency of the TLF through contrastive learning. In addition, a multi-scale confident learning is proposed to correct unreliable labels, which enables the acquisition of broader local structural information instead of relying on individual voxels. To evaluate the effectiveness of our method, we conducted extensive experiments on a self-built database of hundreds of cases of brain CTA scans with IAs. Experimental results demonstrate that our method can effectively learn a robust CTA scan-based IAs segmentation model using unreliable labeled data, outperforming state-of-the-art methods in terms of segmentation accuracy. Codes are released at https://github.com/XueShuangqian/TLF.</p></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"116 ","pages":"Article 102421"},"PeriodicalIF":5.4000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"TLF: Triple learning framework for intracranial aneurysms segmentation from unreliable labeled CTA scans\",\"authors\":\"Lei Chai , Shuangqian Xue , Daodao Tang , Jixin Liu , Ning Sun , Xiujuan Liu\",\"doi\":\"10.1016/j.compmedimag.2024.102421\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Intracranial aneurysm (IA) is a prevalent disease that poses a significant threat to human health. The use of computed tomography angiography (CTA) as a diagnostic tool for IAs remains time-consuming and challenging. Deep neural networks (DNNs) have made significant advancements in the field of medical image segmentation. Nevertheless, training large-scale DNNs demands substantial quantities of high-quality labeled data, making the annotation of numerous brain CTA scans a challenging endeavor. To address these challenges and effectively develop a robust IAs segmentation model from a large amount of unlabeled training data, we propose a triple learning framework (TLF). The framework primarily consists of three learning paradigms: pseudo-supervised learning, contrastive learning, and confident learning. This paper introduces an enhanced mean teacher model and voxel-selective strategy to conduct pseudo-supervised learning on unreliable labeled training data. Concurrently, we construct the positive and negative training pairs within the high-level semantic feature space to improve the overall learning efficiency of the TLF through contrastive learning. In addition, a multi-scale confident learning is proposed to correct unreliable labels, which enables the acquisition of broader local structural information instead of relying on individual voxels. To evaluate the effectiveness of our method, we conducted extensive experiments on a self-built database of hundreds of cases of brain CTA scans with IAs. Experimental results demonstrate that our method can effectively learn a robust CTA scan-based IAs segmentation model using unreliable labeled data, outperforming state-of-the-art methods in terms of segmentation accuracy. Codes are released at https://github.com/XueShuangqian/TLF.</p></div>\",\"PeriodicalId\":50631,\"journal\":{\"name\":\"Computerized Medical Imaging and Graphics\",\"volume\":\"116 \",\"pages\":\"Article 102421\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computerized Medical Imaging and Graphics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0895611124000983\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computerized Medical Imaging and Graphics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0895611124000983","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
TLF: Triple learning framework for intracranial aneurysms segmentation from unreliable labeled CTA scans
Intracranial aneurysm (IA) is a prevalent disease that poses a significant threat to human health. The use of computed tomography angiography (CTA) as a diagnostic tool for IAs remains time-consuming and challenging. Deep neural networks (DNNs) have made significant advancements in the field of medical image segmentation. Nevertheless, training large-scale DNNs demands substantial quantities of high-quality labeled data, making the annotation of numerous brain CTA scans a challenging endeavor. To address these challenges and effectively develop a robust IAs segmentation model from a large amount of unlabeled training data, we propose a triple learning framework (TLF). The framework primarily consists of three learning paradigms: pseudo-supervised learning, contrastive learning, and confident learning. This paper introduces an enhanced mean teacher model and voxel-selective strategy to conduct pseudo-supervised learning on unreliable labeled training data. Concurrently, we construct the positive and negative training pairs within the high-level semantic feature space to improve the overall learning efficiency of the TLF through contrastive learning. In addition, a multi-scale confident learning is proposed to correct unreliable labels, which enables the acquisition of broader local structural information instead of relying on individual voxels. To evaluate the effectiveness of our method, we conducted extensive experiments on a self-built database of hundreds of cases of brain CTA scans with IAs. Experimental results demonstrate that our method can effectively learn a robust CTA scan-based IAs segmentation model using unreliable labeled data, outperforming state-of-the-art methods in terms of segmentation accuracy. Codes are released at https://github.com/XueShuangqian/TLF.
期刊介绍:
The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.