Segmentation of measurable images from standard plane of Graf hip ultrasonograms based on Mask Region-Based Convolutional Neural Network
1Biruni University, Computer Engineering, Istanbul, Türkiye
2Orthopaedics and Traumatology Clinic, Clinique Du Sport Paris, Paris, France
Keywords: Deep learning, developmental dysplasia of the hip, Mask R-CNN, ultrasound.
Objectives: 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.
Patients and methods: 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.
Results: 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.
Conclusion: 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.
Citation: Sezer A, Sezer HB. Segmentation of measurable images from standard plane of Graf hip ultrasonograms based on Mask Region-Based Convolutional Neural Network. Jt Dis Relat Surg 2023;34(3):590-597. doi: 10.52312/jdrs.2023.1308.
The study protocol was approved by the University of Health Sciences Hamidiye Etfal Training and Research Hospital Clinical Research Ethics Committee (date: 13.12.20216, no: 1344). The study was conducted in accordance with the principles of the Declaration of Helsinki.
Idea/concept, design, analysis and/or interpretation, literature review, writing the article, critical review, references and fundings, materials: A.S., H.B.S.; Control/supervision: A.S.; Data collection and/or processing: H.B.S.
The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.
The authors received no financial support for the research and/or authorship of this article.
The data that support the findings of this study are available from the corresponding author upon reasonable request.