Current and emerging trends in medical image segmentation with deep learning

• Published in: IEEE transactions on radiation and plasma medical sciences Vol. 7; no. 6; p. 1
• Main Authors: Conze, Pierre-Henri, Andrade-Miranda, Gustavo, Singh, Vivek Kumar, Jaouen, Vincent, Visvikis, Dimitris
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: artificial intelligence; Computer architecture; Computer Science; Computer Vision and Pattern Recognition; Deep learning; deep neural networks; Distillation; Embedding; Image analysis; Image processing; Image segmentation; Medical diagnosis; Medical diagnostic imaging; Medical Imaging; semantic segmentation; Shape; Task analysis; Transformers; Trends; vision Transformers; semantic segmentation; artificial intelligence; deep neural networks; medical imaging; vision Transformers
• ISSN: 2469-7311; 2469-7303
• DOI: 10.1109/TRPMS.2023.3265863

In recent years, the segmentation of anatomical or pathological structures using deep learning has experienced a widespread interest in medical image analysis. Remarkably successful performance has been reported in many imaging modalities and for a variety of clinical contexts to support clinicians in computer-assisted diagnosis, therapy or surgical planning purposes. However, despite the increasing amount of medical image segmentation challenges, there remains little consensus on which methodology perform best. Therefore, we examine in this paper the numerous developments and breakthroughs brought since the rise of U-Net inspired architectures. Especially, we focus on the technical challenges and emerging trends that the community is now focusing on, including conditional generative adversarial and cascaded networks, medical Transformers, contrastive learning, knowledge distillation, active learning, prior knowledge embedding, cross-modality learning, multi-structure analysis, federated learning or semi-supervised and self-supervised paradigms. We also suggest possible avenues to be further investigated in future research efforts.