Segmentation of ovarian cancer tumors from CT images based on a two-stage neural network fusion system.
Authors
Affiliations (2)
Affiliations (2)
- Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica of Bucharest, Bucharest, Romania.
- Stefan S. Nicolau Institute of Virology, Romanian Academy, Bucharest, Romania.
Abstract
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.