Segmentation of Nucleus in Histopathological Images Using Deep Learning Architectures - Ayaz, Ogun; Usta, Hamdullah; Bilgin, Gokhan (IEEE, 2021)
The aim of this study is to develop a image segmentation system for Histopathological images by using Deep Learning Methods. In today's world cancer is a world wide problem for a lot of people from all around the world. Over several years in scientific studies showed us that scientist tried to find a way to cure cancer or reduce the damage or increase the chance of survival from cancer. As studies shows that early diagnosis of the cancer plays a huge part on the treatment process and highly increases the chance of survival of the cancer patients. Most of the cancer diagnosis system uses a medical image analyze. However most of the cancer types requires is a large amount of test and examination on medical cell images. It is a hard and time consuming process. As a result of this, there is a need has arisen for image segmentation. With the development on computer science, computers started to play a big role for making this process much more fast and accurate. Recent development on Machine Learning and Deep Learning methods can provide efficient image segmentation for large amount of data in a agreeable time line. There was a lot of studies which focused on traditional methods for image segmentation but in this study we aim on the recent development on Deep Learning methods which is used for image segmentation. First of all, an introduction about image segmentation for Histopathological images by Deep Learning Methods is given. After that, literature review about recent studies which is covered our main topic is given. Then, our feasibility studies and system analysis is addressed. Then, implementation and performance studies is given. Finally, an assessment of this study on image segmentation by Deep Learning methods into clinical images are given. The main topic as Deep Learning methods we targeted U-Net, Mask R-CNN, UNet++ and YOLO. We implemented those models to the medical histopathologial image data. We provided the process, result and analyzed outcome and the summary for all the models that we used in this paper.
Keywords: Histopathological images; deep learning; nuclei segmentation; instance segmentation; semantic segmentation; Cell Biology; Engineering