Explainable liver tumor delineation in surgical specimens using hyperspectral imaging and deep learning

高光谱成像 边距(机器学习) 计算机科学 人工智能 模式识别(心理学) 像素 基本事实 肝组织 医学 机器学习 内科学
作者
Yating Zhang,Si Yu,Xueyu Zhu,Xuefei Ning,Wei Liu,Chuting Wang,Xiaohu Liu,Ding Zhao,Yongchang Zheng,Jie Bao
出处
期刊:Biomedical Optics Express [Optica Publishing Group]
卷期号:12 (7): 4510-4510 被引量:14
标识
DOI:10.1364/boe.432654
摘要

Surgical removal is the primary treatment for liver cancer, but frequent recurrence caused by residual malignant tissue remains an important challenge, as recurrence leads to high mortality. It is unreliable to distinguish tumors from normal tissues merely under visual inspection. Hyperspectral imaging (HSI) has been proved to be a promising technology for intra-operative use by capturing the spatial and spectral information of tissue in a fast, non-contact and label-free manner. In this work, we investigated the feasibility of HSI for liver tumor delineation on surgical specimens using a multi-task U-Net framework. Measurements are performed on 19 patients and a dataset of 36 specimens was collected with corresponding pathological results serving as the ground truth. The developed framework can achieve an overall sensitivity of 94.48% and a specificity of 87.22%, outperforming the baseline SVM method by a large margin. In particular, we propose to add explanations on the well-trained model from the spatial and spectral dimensions to show the contribution of pixels and spectral channels explicitly. On that basis, a novel saliency-weighted channel selection method is further proposed to select a small subset of 5 spectral channels which provide essentially as much information as using all 224 channels. According to the dominant channels, the absorption difference of hemoglobin and bile content in the normal and malignant tissues seems to be promising markers that could be further exploited.
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