Deep Vein Thrombosis Segmentation Using Deep Learning for Volume Reconstruction from 3D Freehand Ultrasound Images

Deep vein thrombosis (DVT) refers to the formation of abnormal blood clots within the inner vascular veins, typically in the legs, posing significant health risks. Traditional treatment involves suctioning the clot, monitored by X-ray angiography, which exposes patients and medical staff to radiation. This study aims to enhance DVT diagnosis and treatment by developing a 3D reconstruction method using B-mode ultrasound, linear 3D interpolation, and a multi-denoising filter approach for improved image segmentation. The research methodology includes ultrasound data acquisition with a B-mode scanner and optical tracking system, followed by 3D volume reconstruction through bin-filling and hole-filling processes. Deep learning techniques are employed to segment the blood clot in ultrasound images, and the thrombus volume is estimated. Experiments were conducted in two scenarios: 3D reconstruction using a 2D ultrasound dataset from a DVT patient and thrombus area determination using artificial datasets with fat-injected balloon phantoms. Results demonstrate the proposed method achieved an accuracy of 0.824, a specificity of 0.583, and a sensitivity of 0.955. Thrombus volume estimation yielded a mean absolute percentage error (MAPE) of 27.5%. The findings indicate that the novel method is eligible to be an alternative to reconstruct thrombus volume and accurately identifies thrombus areas in ultrasound images, offering a safer alternative to traditional X-ray-based methods.