Cervical Cancer Vascular Segmentation in 3D Power Doppler Ultrasound Using Advanced Deep Learning Models

Cervical cancer remains a significant global health challenge. Precise segmentation of vascular anatomy in 3D Power Doppler ultrasound (3D - PDU) images is crucial for accurate diagnosis, effective treatment planning and continuous monitoring of the disease. Recent advances in deep learning have significantly enhanced the precision of segmentations for assessing tumor aggressiveness in various types of tumors. However, these advancements are still limited and face challenges when applied to cervical cancer vascular anatomy. Among deep learning models, U-Net excels in pixel-level image analysis, allowing for detailed mapping of vascular structures, which is vital for improving the management and outcomes of cervical cancer patients. This study proposes a framework with two stages: first, extracting the ground-truth masks of the vascular regions from cervical cancer 3D-PDU images, and second, evaluating the performance of three deep learning models in segmenting the vascular regions within these images. After the first stage is validated by gynecological clinicians, three models are evaluated: a U-Net, a U-Net variant with VGG16 backbone, and a U-Net variant with DenseNet121 backbone. The U-Net variant with DenseNet121 backbone achieved the best results on the test dataset, with a pixel accuracy of 97.08%, an Intersection over Union (IoU) of 90.63%, and a Dice coefficient of 95.08%.