An effective approach for the nuclei segmentation from breast histopathological images using star-convex polygon

Abstract

Early detection and patient-driven precise treatment can drastically reduce the elevation in the mortality rate due to breast tumours in women. Therefore, it is inevitable to develop an interpretable model, which detects and segments cancer cell nuclei. The main challenge to achieve this goal is the separation of the individual cell nucleus from the clustered overlapping nuclei without losing the pixel-level shape correspondence of each nucleus. The performance of the most common image processing conventional methods alone has not yielded the expected result for nuclei segmentation from the digitized tissue images either due to over-segmentation or under-segmentation. Here, this issue is addressed by developing an automatic nuclei segmentation algorithm which uses a deep learning-based approach. This paper introduces pixel-level analysis using the star convex polygon approach for segmenting the cell nuclei from the histopathological images. Analysis shows that this technique exemplifies a more accurate segmentation result with a validation IOU score of 87.56%. The true positive rate and the detection of pixel-level shape correspondence are also comparatively high for this method during the test phase.