ContourNet—An Automated Segmentation Framework for Detection of Colonic Polyps

Abstract

One of the most prevalent forms of malignant tumor affecting the digestive system is the Colorectal Cancer (CRC). Due to the recurrent nature of occurrence of CRC tumors the morbidity and mortality rate associated with CRC tumors is very high, making it the fourth leading cause of cancer worldwide. Although colonoscopy, is considered as pre-dominant screening mechanism for detection of CRC, over the recent years several researchers have attempted in developing AI diagnostic tools for segmenting colonic polyps. However, manual intervention and decreased accuracy rate is a major drawback witnessed by these approaches. In this study, we propose a novel automated colonic polyp segmentation mechanism using U-Net and chroma based deformable model termed as ContourNet. The proposed model eliminates the need for manual identification of region of interest irrespective of the high degree of variability between the lesion and non-lesion pixels. The model considers the chromaticity and statistical information from the identified region of interest to evolve the contour close to the affected area. The proposed algorithm was developed using Clinic CVC dataset for training, and a test accuracy of 94% was obtained. The generalization ability of the proposed design is validated on three different datasets, considering all the images as test set, a high degree of accuracy was obtained in identifying the affected regions. Thereby the results obtained prove the ability of the proposed method to be adopted in developing a CAD tool in detection of pathologies associated with gastroenterology in contrast to the state of art methods reported in literature.

Keywords

• Colorectal cancer
• convolutional neural network
• image augmentation
• color space augmentation
• chroma based deformable model
• semantic segmentation