Deep learning-based Segmentation of Multi-site Disease in Ovarian Cancer

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Abstract
Purpose To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. Materials and Methods A deep learning model for the two most common disease sites of high grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established “no-new-Net” (nnU-Net) framework and unrevised trainee radiologist segmentations. A total of 451 pre-treatment and post neoadjuvant chemotherapy (NACT) CT scans collected from four different institutions were used for training (n=276), hyper-parameter tuning (n=104) and testing (n=71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test on paired results Results Our model outperforms the nnU-Net framework by a significant margin for both disease (validation: p=1×10-4,1.5×10-6, test: p=0.004, 0.005) and it does not perform significantly different from a trainee radiologist for the pelvic/ovarian lesions (p=0.392). On an independent test set (n=71), the model achieves a performance of 72±19 mean DSC for the pelvic/ovarian and 64±24 for the omental lesions. Conclusion Automated ovarian cancer segmentation on CT using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. Summary Deep learning-based models were used to assess whether fully automated segmentation is feasible for the main two disease sites in high grade serous ovarian cancer. Key Points First automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images. Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists with three years of experience in oncological and gynecological imaging. Careful hyper-parameter tuning can provide models significantly outperforming strong state-of-the-art baselines.

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