Segmentation of gynaecological ultrasound images using different U-Net based approaches

Ovarian cancer is one of the most commonly occurring cancer in women. Transvaginal ultrasound is used as a screening test to detect the presence of tumors but, for specific types of ovarian tumors, malignancy can only be asserted through surgery. An automatic method to perform the detection and malignancy assessment of these tumours is thus necessary to prevent unnecessary oophorectomies.This work explores the U-Net's architecture and investigates the selection of different hyperparameters for the ovary and the ovarian follicles segmentation. The effect of applying different post-processing methods on beam-formed radio-frequency (BRF) data is also investigated.Results show that models trained only with BRF data have the worst performance. On the other hand, the combination of B-mode with BRF data performs better for ovary segmentation. As for the hyperparameter study, results show that the U-Net with 4 levels is the architecture with the worst performance. This shows that to achieve better performance in the segmentation of ovarian structures, it is important to select an architecture that takes into account the spatial context of the regions of interest. It is also possible to conclude that the method used to analyse BRF data should be designed to take advantage of the fine-resolution of BRF data.