Contouring by Unit Vector Field Regression
This work addresses a domain-specific medical imaging task for sacroiliac joint contouring, presenting an incremental improvement over existing contour regression techniques.
The paper tackles the problem of delineating open contours, specifically sacroiliac joints in spinal MRIs, by introducing a deep-learning method that walks along learnt unit vector fields, achieving an average root mean square error below 4.5 pixels (2.5mm) 95% of the time and outperforming baseline methods.
This work introduces a simple deep-learning based method to delineate contours by `walking' along learnt unit vector fields. We demonstrate the effectiveness of our pipeline on the unique case of open contours on the task of delineating the sacroiliac joints (SIJs) in spinal MRIs. We show that: (i) 95% of the time the average root mean square error of the predicted contour against the original ground truth is below 4.5 pixels (2.5mm for a standard T1-weighted SIJ MRI), and (ii) the proposed method is better than the baseline of regressing vertices or landmarks of contours.