Kakileti Siva Teja, Shrivastava Raghav, Manjunath Geetha, Vidyasagar Mathukumalli, Graewingholt Axel
Niramai Health Analytix Pvt. Ltd., Bangalore, Karnataka, India.
Indian Institute of Technology Hyderabad, Hyderabad, Telangana, India.
J Med Imaging (Bellingham). 2022 Jul;9(4):044502. doi: 10.1117/1.JMI.9.4.044502. Epub 2022 Aug 4.
Vascular changes are observed from initial stages of breast cancer, and monitoring of vessel structures helps in early detection of malignancies. In recent years, thermal imaging is being evaluated as a low-cost imaging modality to visualize and analyze early vascularity. However, visual inspection of thermal vascularity is challenging and subjective. Therefore, there is a need for automated techniques to assist physicians in visualization and interpretation of vascularity by marking the vessel structures and by providing quantified qualitative parameters that helps in malignancy classification In the literature, there are very few approaches for vascular analysis and classification of breast thermal images using interpretable vascular features. One major challenge is the automated detection of breast vascularity due to diffused vessel boundaries. We first propose a deep learning-based semantic segmentation approach that generates heatmaps of vessel structures from two-dimensional breast thermal images for quantitative assessment of breast vascularity. Second, we extract interpretable vascular parameters and propose a classifier to predict likelihood of breast cancer purely from the extracted vascular parameters. The results of the cancer classifier were validated using an independent clinical dataset consisting of 258 participants. The results were encouraging as the proposed approach segmented vessels well and gave a good classification performance with area under receiver operating characteristic curve of 0.85 with the proposed vascularity parameters. The detected vasculature and its associated high classification performance show the utility of the proposed approach in interpretation of breast vascularity.
在乳腺癌的初始阶段就能观察到血管变化，对血管结构的监测有助于早期发现恶性肿瘤。近年来，热成像作为一种低成本的成像方式，正被用于可视化和分析早期血管生成情况。然而，对热血管的目视检查具有挑战性且主观。因此，需要自动化技术来帮助医生通过标记血管结构以及提供有助于恶性肿瘤分类的量化定性参数，来实现血管生成情况的可视化和解读。在文献中，使用可解释血管特征对乳腺热图像进行血管分析和分类的方法非常少。一个主要挑战是由于血管边界模糊，难以自动检测乳腺血管。我们首先提出一种基于深度学习的语义分割方法，该方法从二维乳腺热图像生成血管结构的热图，用于乳腺血管生成情况的定量评估。其次，我们提取可解释的血管参数，并提出一个分类器，仅根据提取的血管参数预测乳腺癌的可能性。使用由258名参与者组成的独立临床数据集对癌症分类器的结果进行了验证。结果令人鼓舞，因为所提出的方法能很好地分割血管，并且在所提出的血管生成参数下，受试者操作特征曲线下面积为0.85，具有良好的分类性能。检测到的血管系统及其相关的高分类性能表明了所提出方法在乳腺血管解读中的实用性。
