Hip segmentation from MRI volumes in infants for DDH diagnosis and treatment planning

Diagnosis and surgical management of Developmental Dysplasia of the Hip (DDH) relies on physical examination and 2D ultrasound scanning. Magnetic Resonance Imaging (MRI) can be used to complement existing techniques and could be advantageous in treatment planning due to its larger field of view. In this paper we propose a semi-automatic method to segment surface models of the acetabulum from MRI images. The method incorporates clinical knowledge in the form of intensity priors which are integrated into a Random Walker (RW) formulation. We use a modified RW framework which compensates for incomplete or blurred boundaries in the image by using information from neighboring slices in the sequence incorporated as node weights. We conducted a pilot study to evaluate the segmentation on a set of 10 infant hip MRI sequences using a 1.5 Tesla MR scanner. Contours obtained from the semi-automated segmentation were compared against manually segmented hip contours using Dice Ratio (DR), Hausdorff Distance (HD) and Root Mean Square (RMS) distance. The proposed method gave values of (DR = 0.84 ± 0.5, HD =3.0 ± 0.7, RMS =1.9 ± 0.3) and (DR=0.86 ± 0.2, HD=3.0 ± 0.1, RMS= 2.0 ± 0.6) for right and left acetabular contours respectively which was higher than the corresponding values obtained from conventional RW segmentation. The execution time of the segmentation algorithm was less than ~4 seconds on a 3.5 GHz CPU.