Vague-Segment Technique: Automatic Computation of Tumor Stroma Ratio for
Breast Cancer on Whole Slides
Abstract
The calculation of Tumor Stroma Ratio (TSR) is a challenging medical
issue that could improve predictions of neoadjuvant chemotherapy
benefits and patient prognoses. Although several studies on breast
cancer and deep learning methods have achieved promising results, the
drawbacks that pixel-level semantic segmentation processes could not
extract core tumor regions containing both tumor pixels and stroma
pixels make it difficult to accurately calculate TSR. In this paper, we
propose a Vague-Segment Technique (VST) consisting of a designed
SwinV2UNet module and a modified Suzuki algorithm. Specifically, the
SwinV2UNet identifies tumor pixels and generate pixel-level
classification results, based on which the modified Suzuki algorithm
extracts the contour of core tumor regions in terms of cosine angle.
Through this way, VST obtains vaguely segmentation results of core tumor
regions containing both tumor pixels and stroma pixels, where the TSR
could be calculated by the formula of Intersection over Union (IOU). For
the training and evaluation, we utilize the well-known The Cancer Genome
Atlas (TCGA) database to create an annotated dataset, while 150 images
with TSR annotations from real cases are also collected. The
experimental results illustrate that the proposed VST could generate
better tumor identification results compared with state-of-the-art
methods, where the extracted core tumor regions lead to more
consistencies of calculated TSR with senior experts compared to junior
pathologists. The experimental results demonstrate the superiority of
our proposed pipeline, which has promise for future clinical
application.