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International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 3/2019

01-03-2019 | Original Article

Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration

Authors: Dimitrios Damopoulos, Till Dominic Lerch, Florian Schmaranzer, Moritz Tannast, Christophe Chênes, Guoyan Zheng, Jérôme Schmid

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 3/2019

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Abstract

Background

Radial 2D MRI scans of the hip are routinely used for the diagnosis of the cam type of femoroacetabular impingement (FAI) and of avascular necrosis (AVN) of the femoral head, both considered causes of hip joint osteoarthritis in young and active patients. A method for automated and accurate segmentation of the proximal femur from radial MRI scans could be very useful in both clinical routine and biomechanical studies. However, to our knowledge, no such method has been published before.

Purpose

The aims of this study are the development of a system for the segmentation of the proximal femur from radial MRI scans and the reconstruction of its 3D model that can be used for diagnosis and planning of hip-preserving surgery.

Methods

The proposed system relies on: (a) a random forest classifier and (b) the registration of a 3D template mesh of the femur to the radial slices based on a physically based deformable model. The input to the system are the radial slices and the manually specified positions of three landmarks. Our dataset consists of the radial MRI scans of 25 patients symptomatic of FAI or AVN and accompanying manual segmentation of the femur, treated as the ground truth.

Results

The achieved segmentation of the proximal femur has an average Dice similarity coefficient (DSC) of 96.37 ± 1.55%, an average symmetric mean absolute distance (SMAD) of 0.94 ± 0.39 mm and an average Hausdorff distance of 2.37 ± 1.14 mm. In the femoral head subregion, the average SMAD is 0.64 ± 0.18 mm and the average Hausdorff distance is 1.41 ± 0.56 mm.

Conclusions

We validated a semiautomated method for the segmentation of the proximal femur from radial MR scans. A 3D model of the proximal femur is also reconstructed, which can be used for the planning of hip-preserving surgery.
Literature
1.
Chughtai M, Piuzzi NS, Khlopas A, Jones LC, Goodman SB, Mont MA (2017) An evidence-based guide to the treatment of osteonecrosis of the femoral head. Bone Jt J 99(10):1267–1279CrossRef
2.
Sullivan JP, Griffith TB, Park CN, Ranawat AS (2017) Advances in 2D and 3D imaging for FAI surgical planning. In: Hip joint restoration. Springer, New York, pp 277–285
3.
Leunig M, Beaulé PE, Ganz R (2009) The concept of femoroacetabular impingement: current status and future perspectives. Clin Orthop Relat Res 467(3):616–622PubMedCrossRef
4.
Tannast M, Siebenrock KA, Anderson SE (2007) Femoroacetabular impingement: radiographic diagnosis—what the radiologist should know. Am J Roentgenol 188(6):1540–1552CrossRef
5.
Steppacher SD, Huemmer C, Schwab JM, Tannast M, Siebenrock KA (2014) Surgical hip dislocation for treatment of femoroacetabular impingement: factors predicting 5-year survivorship. Clin Orthop Relat Res 472(1):337–348PubMedCrossRef
6.
Steppacher SD, Lerch TD, Gharanizadeh K, Liechti EF, Werlen SF, Puls M, Tannast M, Siebenrock KA (2014) Size and shape of the lunate surface in different types of pincer impingement: theoretical implications for surgical therapy. Osteoarthr Cartil 22(7):951–958PubMedCrossRef
7.
8.
Morita D, Hasegawa Y, Okura T, Osawa Y, Ishiguro N (2017) Long-term outcomes of transtrochanteric rotational osteotomy for non-traumatic osteonecrosis of the femoral head. Bone Jt J 99(2):175–183CrossRef
9.
Petchprapa CN, Dunham KS, Lattanzi R, Recht MP (2013) Demystifying radial imaging of the hip. Radiographics 33(3):E97–E112PubMedCrossRef
10.
Chana R, Noorani A, Ashwood N, Chatterji U, Healy J, Baird P (2006) The role of MRI in the diagnosis of proximal femoral fractures in the elderly. Injury 37(2):185–189PubMedCrossRef
11.
Cabarrus MC, Ambekar A, Lu Y, Link TM (2008) MRI and CT of insufficiency fractures of the pelvis and the proximal femur. Am J Roentgenol 191(4):995–1001CrossRef
12.
Sutter R, Dietrich TJ, Zingg PO, Pfirrmann CW (2012) How useful is the alpha angle for discriminating between symptomatic patients with cam-type femoroacetabular impingement and asymptomatic volunteers? Radiology 264(2):514–521PubMedCrossRef
13.
Klenke FM, Hoffmann DB, Cross BJ, Siebenrock KA (2015) Validation of a standardized mapping system of the hip joint for radial MRA sequencing. Skelet Radiol 44(3):339–343CrossRef
14.
Domayer SE, Mamisch TC, Kress I, Chan J, Kim YJ (2010) Radial dGEMRIC in developmental dysplasia of the hip and in femoroacetabular impingement: preliminary results. Osteoarthr Cartil 18(11):1421–1428PubMedCrossRef
15.
Zilkens C, Tiderius CJ, Krauspe R, Bittersohl B (2015) Current knowledge and importance of dGEMRIC techniques in diagnosis of hip joint diseases. Skelet Radiol 44(8):1073–1083CrossRef
16.
Riley GM, McWalter EJ, Stevens KJ, Safran MR, Lattanzi R, Gold GE (2015) MRI of the hip for the evaluation of femoroacetabular impingement; past, present, and future. J Magn Reson Imaging 41(3):558–572PubMedCrossRef
17.
Schmaranzer F, Todorski IAS, Lerch TD, Schwab J, Cullmann-Bastian J, Tannast M (2017) Intra-articular lesions: imaging and surgical correlation. In: Seminars in musculoskeletal radiology, vol 21, No. 05. Thieme Medical Publishers, pp 487–506
18.
Schmaranzer F, Haefeli PC, Hanke MS, Liechti EF, Werlen SF, Siebenrock KA, Tannast M (2017) How does the dGEMRIC index change after surgical treatment for FAI? A prospective controlled study: preliminary results. Clin Orthop Relat Res 475(4):1080–1099PubMedCrossRef
19.
Rathnayaka K, Momot KI, Noser H, Volp A, Schuetz MA, Sahama T, Schmutz B (2012) Quantification of the accuracy of MRI generated 3D models of long bones compared to CT generated 3D models. Med Eng Phys 34(3):357–363PubMedCrossRef
20.
Lerch T, Degonda C, Zheng G, Todorski I, Schmaranzer F, Ecker T, Siebenrock K, Tannast M (2017) MR-based 3D PAO planning and simulation of hip impingement is as accurate as CT-based 3D models. German Congress of Orthopedic and Trauma Surgery (DKOU 2017)
21.
Xia Y, Fripp J, Chandra SS, Schwarz R, Engstrom C, Crozier S (2013) Automated bone segmentation from large field of view 3D MR images of the hip joint. Phys Med Biol 58(20):7375PubMedCrossRef
22.
Schmid J, Kim J, Magnenat-Thalmann N (2011) Robust statistical shape models for MRI bone segmentation in presence of small field of view. Med Image Anal 15(1):155–168PubMedCrossRef
23.
Gilles B, Magnenat-Thalmann N (2010) Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations. Med Image Anal 14(3):291–302PubMedCrossRef
24.
Arezoomand S, Lee WS, Rakhra KS, Beaulé PE (2015) A 3D active model framework for segmentation of proximal femur in MR images. Int J Comput Assist Radiol Surg 10(1):55–66PubMedCrossRef
25.
Chandra SS, Xia Y, Engstrom C, Crozier S, Schwarz R, Fripp J (2014) Focused shape models for hip joint segmentation in 3D magnetic resonance images. Med Image Anal 18(3):567–578PubMedCrossRef
26.
Zeng G, Yang X, Li J, Yu L, Heng PA, Zheng G (2017) 3D U-net with multi-level deep supervision: fully automatic segmentation of proximal femur in 3D MR images. In: International workshop on machine learning in medical imaging. Springer, Cham, pp 274–282
27.
Paiement A, Mirmehdi M, Xie X, Hamilton MC (2014) Integrated segmentation and interpolation of sparse data. IEEE Trans Image Process 23(1):110–125PubMedCrossRef
28.
Van Assen HC, Danilouchkine MG, Frangi AF, Ordás S, Westenberg JJ, Reiber JH, Lelieveldt BP (2006) SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med Image Anal 10(2):286–303PubMedCrossRef
29.
Tu Z (2008) Auto-context and its application to high-level vision tasks. In: IEEE conference on computer vision and pattern recognition, CVPR 2008. IEEE, pp 1–8
30.
Gao Y, Wang L, Shao Y, Shen D (2014) Learning distance transform for boundary detection and deformable segmentation in ct prostate images. In: International workshop on machine learning in medical imaging. Springer, Cham, pp 93–100
31.
Nyúl LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. IEEE Trans Med Imaging 19(2):143–150PubMedCrossRef
32.
Glocker B, Zikic D, Konukoglu E, Haynor DR, Criminisi A (2013) Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 262–270
33.
Criminisi A, Robertson D, Konukoglu E, Shotton J, Pathak S, White S, Siddiqui K (2013) Regression forests for efficient anatomy detection and localization in computed tomography scans. Med Image Anal 17(8):1293–1303PubMedCrossRef
34.
Schmid J, Magnenat-Thalmann N (2008) MRI bone segmentation using deformable models and shape priors. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 119–126
35.
Volino P, Magnenat-Thalmann N (2000) Implementing fast cloth simulation with collision response. In: Proceedings of the computer graphics international. IEEE, pp 257–266
36.
Cootes TF, Hill A, Taylor CJ, Haslam J (1993) The use of active shape models for locating structures in medical images. In: Biennial international conference on information processing in medical imaging. Springer, Berlin, pp 33–47
37.
Kraevoy V, Sheffer A (2006) Mean-value geometry encoding. Int J Shape Model 12(01):29–46CrossRef
38.
Kumar S (2003) Discriminative random fields: a discriminative framework for contextual interaction in classification. In: Proceedings of the 9th IEEE international conference on computer vision, 2003. IEEE, pp 1150–1157
39.
Chu C, Chen C, Liu L, Zheng G (2015) Facts: fully automatic ct segmentation of a hip joint. Ann Biomed Eng 43(5):1247–1259PubMedCrossRef
40.
Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128PubMedCrossRef
41.
Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30(9):1323–1341PubMedPubMedCentralCrossRef
42.
Zikic D, Glocker B, Konukoglu E, Criminisi A, Demiralp C, Shotton J, Thomas OM, Das T, Jena R, Price SJ (2012) Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 369–376
43.
Mahapatra D (2014) Analyzing training information from random forests for improved image segmentation. IEEE Trans Image Process 23(4):1504–1512PubMedCrossRef
44.
Montillo A, Shotton J, Winn J, Iglesias JE, Metaxas D, Criminisi A (2011) Entangled decision forests and their application for semantic segmentation of CT images. In: Biennial international conference on information processing in medical imaging. Springer, Berlin, pp 184–196
45.
Zikic D, Glocker B, Criminisi A (2014) Encoding atlases by randomized classification forests for efficient multi-atlas label propagation. Med Image Anal 18(8):1262–1273PubMedCrossRef
46.
Geremia E, Clatz O, Menze BH, Konukoglu E, Criminisi A, Ayache N (2011) Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. NeuroImage 57(2):378–390PubMedCrossRef
47.
48.
Criminisi A, Shotton J (eds) (2013) Decision forests for computer vision and medical image analysis. Springer, Berlin
49.
Damopoulos D, Glocker B, Zheng G (2017) Automatic localization of the lumbar vertebral landmarks in CT images with context features. In: International workshop and challenge on computational methods and clinical applications in musculoskeletal imaging. Springer, Cham, pp 59–71
50.
51.
52.
53.
Li H, Johnson T (2014) Wilcoxon’s signed-rank statistic: what null hypothesis and why it matters. Pharmaceutical statistics 13(5):281–285PubMedCrossRef
54.
Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures. CRC Press, Boca RatonCrossRef
Metadata
Title
Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration
Authors
Dimitrios Damopoulos
Till Dominic Lerch
Florian Schmaranzer
Moritz Tannast
Christophe Chênes
Guoyan Zheng
Jérôme Schmid
Publication date
01-03-2019
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 3/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-018-1899-z

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