The field of medical image segmentation is rapidly advancing with the development of new deep learning architectures and techniques. Recent research has focused on improving the accuracy and efficiency of segmentation models, particularly in the context of unsupervised learning and real-time inference. One notable trend is the integration of traditional machine learning approaches with deep learning methods, enabling the creation of more robust and accurate models. Another area of focus is the development of models that can handle complex and variable anatomical structures, such as those found in gastrointestinal and airway imaging. Noteworthy papers in this area include:

• Unsupervised Segmentation of Micro-CT Scans of Polyurethane Structures By Combining Hidden-Markov-Random Fields and a U-Net, which presents a novel approach to unsupervised segmentation using HMRF and U-Net.
• EndoSight AI: Deep Learning-Driven Real-Time Gastrointestinal Polyp Detection and Segmentation for Enhanced Endoscopic Diagnostics, which achieves high accuracy and real-time inference speeds for polyp detection and segmentation.
• RepAir: A Framework for Airway Segmentation and Discontinuity Correction in CT, which proposes a robust 3D airway segmentation framework that combines an nnU-Net-based network with anatomically informed topology correction.