3D brain tumor segmentation based on a novel nettree merging

Brain tumors rank among the most devastating diseases in the human body, with their growth potentially resulting in impaired brain tissue function and even life-threatening scenarios, profoundly impacting patients physical and mental well-being. Presently, segmentation methods for three-dimensional (3D) brain tumors can better reflect the position and relationship of the entire brain tissue, providing more reference information. However, challenges persist with existing 3D segmentation techniques, including the lack of direct manipulation on 3D images and suboptimal performance on clinical datasets. Addressing this, this paper proposes a 3D brain tumor segmentation method based on nettree merging. This approach leverages a topological determination between body targets to establish relationships, constructs a nettree structure based on pathological topology, and subsequently merges topological block according to its structure and intensity, thereby segmenting the Gross Tumor Volume (GTV) of 3D MRI brain tumor images. The proposed method is validated using Dice, HD95, Precision, and Recall, and the values of these metrics on the clinical dataset are approximately 0.824, 10.981, 0.829, 0.834, while on the BraTS dataset, they are close to 0.873, 4.902, 0.871, 0.864. Experimental validation on both public and clinical datasets substantiates the effectiveness of the proposed method.