Introduction to Medical Image Segmentation: Overview of Modalities, Benchmark Datasets, Data Augmentation Techniques, and Evaluation Metrics

Summary

Medical image segmentation plays a pivotal role in modern healthcare, enabling precise analysis and diagnosis through the extraction of anatomical structures and abnormalities. This chapter provides a comprehensive overview of the landscape of medical image segmentation, focusing on various modalities, datasets, augmentation techniques, and performance metrics employed in the field. First, it delves into the diverse modalities utilized in medical imaging, encompassing modalities such as magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US), and others, each with unique characteristics. Furthermore, the chapter explores the significance of datasets in training and evaluation, highlighting popular datasets such as Medical Image Computing and Computer‐Assisted Intervention (MICCAI), multimodal brain tumor segmentation (BRATS), and others tailored for specific medical imaging modalities and pathologies. This chapter also discusses the various data augmentation techniques for addressing data scarcity, including methods such as geometric transformations, intensity variations, and generative models. Moreover, the chapter investigates various performance metrics utilized for evaluating segmentation algorithms. Through synthesizing these diverse aspects, this chapter aims to provide comprehensive insights for researchers, practitioners, and stakeholders striving to advance this critical area of medical imaging.