A Systematic Review of U-Net Optimizations: Advancing Tumour Segmentation in Medical Imaging

Since its inception in 2015, U-Net has emerged as a cornerstone architecture that is particularly well-designed for medical image segmentation. Despite its robustness, precise tumour segmentation persists as a challenge because of tumour heterogeneity, boundary ambiguity, and the partial volume effects exhibited by tumours. Therefore, the U-Net architecture has been altered many times to expand its capabilities with complex segmentation challenges, particularly with tumours. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this systematic review critically evaluates and analyses the effectiveness of recent enhancement strategies developed to optimize the performance of the traditional U-Net architecture in attaining accurate tumour segmentation in CT and MRI images. The strategies have been divided into five main areas: U-Net architectural enhancements, including U-Net backbone optimization; skip connection refinements; bottleneck optimizations; transformer-based integrations; and metaheuristic algorithms as a self-adaptive optimization technique. Afterward, each category is thoroughly examined to determine how the strategies address specific limitations inherent to the traditional U-Net model. In addition, this paper reviews the pivotal role of preprocessing techniques in determining segmentation performance. This review identifies persistent research gaps and offers valuable insights for future research to improve the robustness, accuracy, and clinical applicability of the U-Net model.