Deep learning for medical image segmentation: State-of-the-art advancements and challenges

Abstract

Image segmentation, a crucial process of dividing images into distinct parts or objects, has witnessed remarkable advancements with the emergence of deep learning (DL) techniques. The use of layers in deep neural networks, like object form recognition in higher layers and basic edge identification in lower layers, has markedly improved the quality and accuracy of image segmentation. Consequently, DL using picture segmentation has become commonplace, video analysis, facial recognition, etc. Grasping the applications, algorithms, current performance, and challenges are crucial for advancing DL-based medical image segmentation. However, there is a lack of studies delving into the latest state-of-the-art developments in this field. Therefore, this survey aimed to thoroughly explore the most recent applications of DL-based medical image segmentation, encompassing an in-depth analysis of various commonly used datasets, pre-processing techniques and DL algorithms. This study also investigated the state-of-the-art advancement done in DL-based medical image segmentation by analyzing their results and experimental details. Finally, this study discussed the challenges and future research directions of DL-based medical image segmentation. Overall, this survey provides a comprehensive insight into DL-based medical image segmentation by covering its application domains, model exploration, analysis of state-of-the-art results, challenges, and research directions—a valuable resource for multidisciplinary studies.

Keywords

• Image segmentation
• Medical imaging
• Image pre-processing
• Data augmentation
• Deep learning