A Two-Phase Methodology leveraging X-DenseNet for Multi-Organ Segmentation in Abdominal CT Scans

Introduction: Multi organ segmentation (MOS) is crucial for various medical applications such as disease diagnosis, treatment planning, and surgery. Manual segmentation of CT organs is inefficient, laborious and time-consuming, necessitating the development of automated techniques. To address these challenges, automatic techniques based on deep learning models are explored in this field. These techniques require improvements to deal with patient-to-patient variability in organ size, location, and shape. Despite the transformative potential of deep learning techniques, research studies often inadequately address low contrast and overlapping structures within CT scans. This paper proposes a novel two phase methodology to integrate deep learning models and image enhancement strategies. Methods: Our framework contributes in two stages named (a) Optimized Contrast Limited Adaptive Histogram Equalization-Weighted Grey Wolf Optimization (optiCLAHE-Weighted GWO), where the organs contrast is enhanced by employing weighted GWO for CLAHE clip limit selection (b) X-DenseNet architecture, where segmented regions of interest are produced with X-DenseNet model from the enhanced CT. Results: For validation of proposed multi organ segmentation (PMOS) technique, experimentation and comparative analysis with existing models has been conducted on FLARE 22 challenge dataset. The Dice Score (DSC) of liver, aorta and spleen for proposed Multi organ segmentation (PMOS) approach is 90.4%, 99.38% and 98.33% respectively. The mean of Precision (Pre), Accuracy (Acc), F-score and DSC of the proposed approach are 95.05%, 95.55%, 95.29% and 96.06% respectively.