Segmentation of ovarian cancer tumors from CT images based on a two-stage neural network fusion system

The detection of ovarian cancer using computed tomography images is making rapid progress, combining the strengths of different neural networks within a complex ensemble, a more effective model can be created to support medical decision-making in ovarian cancer cases. Various neural networks and ensemble models were tested on the TCGA OV database. Tumor regions were segmented using computer-assisted annotation masks, while molecular subtype labels were assigned at the patient level based on TCGA genomic classifications and mapped to tumors. As the main novelty, the proposed method involves a two-stage fusion system. The first stage combines several neural networks into four ensembles to capture diverse features, and the second stage integrates these results into a global system to enhance segmentation accuracy. The selected models demonstrated strong segmentation performance within the investigated experimental setting and were subsequently integrated into the proposed two-stage fusion framework. The neural networks for the first stage are Attention U-Net, DeepLabV3+, and ViT. The results show that the proposed two-stage fusion system achieved higher observed segmentation performance than the evaluated individual neural networks and first-stage ensemble configurations within the present TCGA-OV experimental setting. Across the five evaluated classes, the Global System achieved an average F1 score of 93.53%, recall of 93.38%, and precision of 93.68%, supporting the technical feasibility of the proposed two-stage fusion approach for ovarian tumor segmentation within the investigated dataset.