Cascaded CNN for View Independent Breast Segmentation in Thermal Images

Abstract

Breast Cancer is the leading cause of cancer deaths in women today. Use of thermal imaging for early stage breast cancer screening is gaining more adoption in recent times and automated analysis of these thermal images with computer aided diagnosis is the key to maintain objectivity in assessment and improve quality of diagnosis. One of the main challenges in automated breast thermography is accurate segmentation of breast region robust to technician errors in image capture - such as view, distance from imaging device, position, etc. Existing algorithms for segmentation are mostly based on heuristic rules and are highly dependent upon the image capture correctness. We propose a cascaded CNN architecture to perform accurate segmentation robust to subject views and capture errors. The proposed approach can detect breasts region independent of the image capture and view angle, enabling automated image and video analysis. We also detailed and compared our algorithm with a multi-view heuristics-based segmentation method. Our proposed technique resulted a dice index of 0.92 when compared with expert segmentation on a test set comprising of 900 images collected from 150 subjects at five different view angles.