Open AccessSegmentation of Polyp Instruments using UNet based deep learning model
Author(s) -
Ashar Mirza,
Rishav Kumar Rajak
Publication year - 2021
Publication title -
nordic machine intelligence
Language(s) - English
Resource type - Journals
ISSN - 2703-9196
DOI - 10.5617/nmi.9145
Subject(s) - segmentation , artificial intelligence , market segmentation , computer science , task (project management) , image segmentation , computer vision , deep learning , scale space segmentation , focus (optics) , segmentation based object categorization , pattern recognition (psychology) , set (abstract data type) , engineering , physics , systems engineering , optics , marketing , business , programming language
In this paper, we present a UNet architecture-based deep learning method that is used to segment polyp and instruments from the image data set provided in the MedAI Challenge2021. For the polyp segmentation task, we developed a UNet based algorithm for segmenting polyps in images taken from endoscopies. The main focus of this task is to achieve high segmentation metrics on the supplied test dataset. Similarly for the polyp segmentation task, in the instrument segmentation task, we have developed UNet based algorithms for segmenting instruments present in colonoscopy videos.