Medical image segmentation schemes for the analysis of gynaecological malignancies

Abstract

Medical imaging yields numerous contributions in modern, state-of-the-art cancer care. It plays a vital role in screening, detection, diagnosis, treatment, and follow-ups. Image segmentation is the pre-processing task for many computer assisted medical imaging applications. It automates or facilitates the demarcation of anatomical structures and other regions of interest in medical images. We illustrate herein a critical review of few such methods, which are very promising and vigorously researched methods for medical image segmentation. These methods are highly analytical and involve extensive computations, which make the solutions less intuitive for the practitioners and hard to compare their applicability. To find the applicability of such methods in the analysis of gynaecological malignancies, numerous experiments have been conducted on contrast enhanced computed tomography (CECT) images collected for gynaecological cancer patients. The results reveal the strengths and limitations of such methods in extracting the regions of interest for the analysis of gynaecological cancers.