Mammographic mass detection and segmentation are usually performed as serial and separate tasks, with segmentation often only performed on manually confirmed true positive detections in previous studies. We propose a fully-integrated computer-aided detection (CAD) system for simultaneous mammographic mass detection and segmentation without user intervention. The proposed CAD only consists of a pseudo-color image generation and a mass detection-segmentation stage based on Mask R-CNN. Grayscale mammograms are transformed into pseudo-color images based on multi-scale morphological sifting where mass-like patterns are enhanced to improve the performance of Mask R-CNN. Transfer learning with the Mask R-CNN is then adopted to simultaneously detect and segment masses on the pseudo-color images. Evaluated on the public dataset INbreast, the method outperforms the state-of-the-art methods by achieving an average true positive rate of 0.90 at 0.9 false positive per image and an average Dice similarity index of 0.88 for mass segmentation.
@article{arxiv.1906.12118,
title = {Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN},
author = {Hang Min and Devin Wilson and Yinhuang Huang and Siyu Liu and Stuart Crozier and Andrew P Bradley and Shekhar S. Chandra},
journal= {arXiv preprint arXiv:1906.12118},
year = {2019}
}