Segmentation of measurable images from standard plane of Graf hip ultrasonograms based on Mask Region-Based Convolutional Neural Network.

Abstract

This study proposed a Mask Region-Based Convolutional Neural Network (R-CNN)-based automatic segmentation to accurately detect the measurable standard plane of Graf hip ultrasonography images via segmentation of the labrum, lower limb of ilium, and the iliac wing. The study examined the hip ultrasonograms of 675 infants (205 males, 470 females; mean age: 7±2.8 weeks; range, 3 to 20 weeks) recorded between January 2011 and January 2018. The standard plane newborn hip ultrasound images were classified according to Graf's method by an experienced ultrasonographer. The hips were grouped as type 1, type 2a, type 2b, and type 2c-D. Two hundred seventy-five ultrasonograms were utilized as training data, 30 were validation data, and 370 were test data. The three anatomical regions were simultaneously segmented by Mask-R CNN in the test data and defective ultrasonograms. Automatic instance-based segmentation results were compared with the manual segmentation results of an experienced orthopedic expert. Success rates were calculated using Dice and mean average precision (mAP) metrics. Of these, 447 Graf type 1, 175 type 2a or 2b, 53 type 2c and D ultrasonograms were utilized. Average success rates with respect to hip types in the whole data were 96.95 and 96.96% according to Dice and mAP methods, respectively. Average success rates with respect to anatomical regions were 97.20 and 97.35% according to Dice and mAP methods, respectively. The highest average success rates were for type 1 hips, with 98.46 and 98.73%, and the iliac wing, with 98.25 and 98.86%, according to Dice and mAP methods, respectively. Mask R-CNN is a robust instance-based method in the segmentation of Graf hip ultrasonograms to delineate the standard plane. The proposed method revealed high success in each type of hip for each anatomic region.