A Tensor Sparse Representation-Based CBMIR System for Computer-Aided Diagnosis of Focal Liver Lesions and its Pilot Trial
A Tensor Sparse Representation-Based CBMIR System for Computer-Aided Diagnosis of Focal Liver Lesions and its Pilot Trial
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DOI：
10.1145/3460426.3463673
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发表时间：
2021-08
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影响因子：
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通讯作者：
Jian Wang;X. Han;Lanfen Lin;Hongjie Hu;Yenwei Chen
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文献类型：
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作者：
Jian Wang;X. Han;Lanfen Lin;Hongjie Hu;Yenwei Chen
由于肝脏局灶性病变的复杂性，临床医生为了做出正确的诊断和采取适当的治疗，需要参考诊断出的医学病例。然而，对于医生来说，从积累的超大医学数据集中找到类似的有意义的病例是一个沉重的负担。基于内容的医学图像检索(CBMIR)是在大型数据库中搜索相似图像的技术，近年来引起了越来越多的研究兴趣。CBMIR系统为医生提供诊断病例，以提高诊断的准确性和置信度。本文提出了一种张量稀疏表示方法来提取多相CT图像的时间和空间特征，从而为医生提供与查询病例更相关的病例。将所提出的张量稀疏表示方法应用于肝局灶性病变的检索。实验表明，该方法取得了比传统方法更好的检索效果。中试结果表明，基于该方法开发的CBMIR系统显著提高了诊断的准确率和置信度。
Clinicians refer to diagnosed medical cases in order to make correct diagnosis and take appropriate treatments, due to the complexity of focal liver lesions. It's a heavy burden, however, for medical doctors to find out similar and meaningful cases from the accumulated extreme large medical datasets. Content based medical image retrieval (CBMIR) that searches for similar images in a large database has been attracting increasing research interest recently. A CBMIR system provides doctors the diagnosed cases to improve the diagnosis accuracy and confidence. This paper proposed a tensor sparse representation method to extract temporal and spatial features of multi-phase CT images, so as to provide doctors medical cases more relevant to the query one. The proposed tensor sparse representation method is applied to the retrieval of focal liver lesions (FLLs). Experiments show that the proposed method achieved better retrieval performance than conventional methods. Pilot trial was conducted and results show that diagnosis accuracy and confidence was improved significantly by the developed CBMIR system based on the proposed method.