An Overview of Image Segmentation Algorithms Used in the Medical Field

Abstract

There has been a rise in the usage of image segmentation algorithms in the medical field. Medical image segmentation includes many types, such as MRI and CR image segmentation. Studying those images help improve the ability to detect diseases earlier, in addition to increasing the knowledge about organ functions. Some of those applications include recognizing brain development, tumor segmentation, and surgical planning. This study aims to provide information about some of the most famously used techniques in the field of medical image segmentation, which can help improve the quality of applications constructed in the medical segmentation field. Four models were focused on in this review, which are: U-Net, V-Net, FCN, and DenseNet. The paper differentiates between the models through architecture, accuracy, and training environment.