Optimizing Geometric Constraints: A Systematic Review on the Impact of Linear Curvature Regularization in Variational Joint Segmentation-Registration Models for Medical Images

Abstract

Biomedical imaging has played a vital role in disease detection, tracking, and treatment. These methods are essential for patient care and health benefits. A synthesis of the literature is helpful to find out limitations such as difficulty handling images with complex background structures and intensity inhomogeneity. The primary aim of this review is to tackle these inadequacies. Then, to identify gaps for future studies. A main goal is to review a joint segmentation–registration model that highlights computational and methodological challenges. Also, to identify obstacles to the clinical translation of the new joint segmentation and registration (NJSR) model. A systematic literature review was carried out to specify relevant studies in medical image analysis. Searches were conducted in IEEE Xplore and Google Scholar. Key search terms incorporated "medical image analysis" and "joint segmentation and registration." The review incorporated models across anatomical structures (e.g., brain magnetic resonance imaging (MRI), hand X-ray), and comparative and narrative reviews of main foundational studies. It excluded the non-joint studies. The literature studies show that separate approaches as compared to joint segmentation and registration frameworks, do not show much significant elevation in performance. The comparative and narrative analyses focused on sum of squared differences (SSD) metrics and the deformation vector field. All in all, the previous studies show that joint frameworks can cope with the reliability, accuracy, and robustness of disease detection and tracking process in biomedical image analysis. All in all, NJSR has demonstrated better performance in convergence speed and registration smoothness compared to Guyder Vese joint segmentation and registration (GV-JSR) model, while NJSR also having the limitations in segmentation accuracy for complex real-world data (Experiment 2 and 4) that is in certain complex experiments the F measure is observed to be the lower in value (that is lower than 0.5). This slight sacrifice in the segmentation accuracy (F-measure) is an acceptable trade-off within the context of pure registration task focused on biomechanical plausibility (as shown by lower E score). In regions where the main objective is to derive a physically meaningful motion field for non-rigid compensation—rather than accurate segmentation borders—this enforcement shifts to achieving a smoother, more reliable deformation field. However, it is acknowledged that for clinical segmentation tasks (e.g., tumor volume definition), this level of precision would be insufficient and require further refinement of the data fidelity term. Moreover, the models having NJSR as a base paper having multi modal frameworks are also come into comparison with each other.