Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images

Najini Harischandra; Anuja Dharmaratne; Flavia M. Cicuttini; Yuan Yuan Wang

doi:10.1007/978-3-030-63820-7_69

Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images

Najini Harischandra, Anuja Dharmaratne, Flavia M. Cicuttini, Yuan Yuan Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

1 Citation (Scopus)

Abstract

Hip Osteoarthritis (OA) is a common pathological condition among the elderly population, which is mainly characterized by cartilage degeneration. Accurate segmentation of the cartilage tissue over MRIs facilitates quantitative investigations into the disease progression. We propose an automated approach to segment the hip joint cartilage as a single unit from routine clinical MRIs utilizing a voxel-based classification approach. We extracted a rich feature set from the MRIs, which consisting of normalized image intensity-based, local image structure-based, and geometry-based features. We have evaluated the proposed method using routine clinical hip MR images taken from asymptomatic elderly and diagnosed OA patients. MR images from both cohorts show full or partial loss of thickness due to aging or hip OA progression. The proposed algorithm shows good accuracy compared to the manual segmentations with a mean DSC value of 0.74, even with a high prevalence of cartilage defects in the MRI dataset.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part IV
Editors	Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	606-614
Number of pages	9
ISBN (Electronic)	9783030638207
ISBN (Print)	9783030638191
DOIs	https://doi.org/10.1007/978-3-030-63820-7_69
Publication status	Published - 2020
Event	International Conference on Neural Information Processing 2020 - Bangkok, Thailand Duration: 18 Nov 2020 → 22 Nov 2020 Conference number: 27th https://link.springer.com/book/10.1007/978-3-030-63830-6 (Proceedings)

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	1332
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	International Conference on Neural Information Processing 2020
Abbreviated title	ICONIP 2020
Country/Territory	Thailand
City	Bangkok
Period	18/11/20 → 22/11/20
Internet address	https://link.springer.com/book/10.1007/978-3-030-63830-6 (Proceedings)

Keywords

Cartilage segmentation
Hip osteoarthritis
MRI

Access to Document

10.1007/978-3-030-63820-7_69

Cite this

Harischandra, N., Dharmaratne, A., Cicuttini, F. M., & Wang, Y. Y. (2020). Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images. In H. Yang, K. Pasupa, A. C.-S. Leung, J. T. Kwok, J. H. Chan, & I. King (Eds.), Neural Information Processing : 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part IV (pp. 606-614). (Communications in Computer and Information Science; Vol. 1332). Springer. https://doi.org/10.1007/978-3-030-63820-7_69

Harischandra, Najini ; Dharmaratne, Anuja ; Cicuttini, Flavia M. et al. / Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images. Neural Information Processing : 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part IV. editor / Haiqin Yang ; Kitsuchart Pasupa ; Andrew Chi-Sing Leung ; James T. Kwok ; Jonathan H. Chan ; Irwin King. Cham Switzerland : Springer, 2020. pp. 606-614 (Communications in Computer and Information Science).

@inproceedings{7036d467545f486e9da16b234df74d4b,

title = "Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images",

abstract = "Hip Osteoarthritis (OA) is a common pathological condition among the elderly population, which is mainly characterized by cartilage degeneration. Accurate segmentation of the cartilage tissue over MRIs facilitates quantitative investigations into the disease progression. We propose an automated approach to segment the hip joint cartilage as a single unit from routine clinical MRIs utilizing a voxel-based classification approach. We extracted a rich feature set from the MRIs, which consisting of normalized image intensity-based, local image structure-based, and geometry-based features. We have evaluated the proposed method using routine clinical hip MR images taken from asymptomatic elderly and diagnosed OA patients. MR images from both cohorts show full or partial loss of thickness due to aging or hip OA progression. The proposed algorithm shows good accuracy compared to the manual segmentations with a mean DSC value of 0.74, even with a high prevalence of cartilage defects in the MRI dataset.",

keywords = "Cartilage segmentation, Hip osteoarthritis, MRI",

author = "Najini Harischandra and Anuja Dharmaratne and Cicuttini, {Flavia M.} and Wang, {Yuan Yuan}",

year = "2020",

doi = "10.1007/978-3-030-63820-7_69",

language = "English",

isbn = "9783030638191",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "606--614",

editor = "Haiqin Yang and Kitsuchart Pasupa and Leung, {Andrew Chi-Sing} and Kwok, {James T.} and Chan, {Jonathan H.} and Irwin King",

booktitle = "Neural Information Processing",

note = "International Conference on Neural Information Processing 2020, ICONIP 2020 ; Conference date: 18-11-2020 Through 22-11-2020",

url = "https://link.springer.com/book/10.1007/978-3-030-63830-6",

}

Harischandra, N, Dharmaratne, A , Cicuttini, FM & Wang, YY 2020, Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images. in H Yang, K Pasupa, AC-S Leung, JT Kwok, JH Chan & I King (eds), Neural Information Processing : 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part IV. Communications in Computer and Information Science, vol. 1332, Springer, Cham Switzerland, pp. 606-614, International Conference on Neural Information Processing 2020, Bangkok, Thailand, 18/11/20. https://doi.org/10.1007/978-3-030-63820-7_69

Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images. / Harischandra, Najini; Dharmaratne, Anuja ; Cicuttini, Flavia M. et al.
Neural Information Processing : 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part IV. ed. / Haiqin Yang; Kitsuchart Pasupa; Andrew Chi-Sing Leung; James T. Kwok; Jonathan H. Chan; Irwin King. Cham Switzerland: Springer, 2020. p. 606-614 (Communications in Computer and Information Science; Vol. 1332).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images

AU - Harischandra, Najini

AU - Dharmaratne, Anuja

AU - Cicuttini, Flavia M.

AU - Wang, Yuan Yuan

N1 - Conference code: 27th

PY - 2020

Y1 - 2020

N2 - Hip Osteoarthritis (OA) is a common pathological condition among the elderly population, which is mainly characterized by cartilage degeneration. Accurate segmentation of the cartilage tissue over MRIs facilitates quantitative investigations into the disease progression. We propose an automated approach to segment the hip joint cartilage as a single unit from routine clinical MRIs utilizing a voxel-based classification approach. We extracted a rich feature set from the MRIs, which consisting of normalized image intensity-based, local image structure-based, and geometry-based features. We have evaluated the proposed method using routine clinical hip MR images taken from asymptomatic elderly and diagnosed OA patients. MR images from both cohorts show full or partial loss of thickness due to aging or hip OA progression. The proposed algorithm shows good accuracy compared to the manual segmentations with a mean DSC value of 0.74, even with a high prevalence of cartilage defects in the MRI dataset.

AB - Hip Osteoarthritis (OA) is a common pathological condition among the elderly population, which is mainly characterized by cartilage degeneration. Accurate segmentation of the cartilage tissue over MRIs facilitates quantitative investigations into the disease progression. We propose an automated approach to segment the hip joint cartilage as a single unit from routine clinical MRIs utilizing a voxel-based classification approach. We extracted a rich feature set from the MRIs, which consisting of normalized image intensity-based, local image structure-based, and geometry-based features. We have evaluated the proposed method using routine clinical hip MR images taken from asymptomatic elderly and diagnosed OA patients. MR images from both cohorts show full or partial loss of thickness due to aging or hip OA progression. The proposed algorithm shows good accuracy compared to the manual segmentations with a mean DSC value of 0.74, even with a high prevalence of cartilage defects in the MRI dataset.

KW - Cartilage segmentation

KW - Hip osteoarthritis

KW - MRI

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U2 - 10.1007/978-3-030-63820-7_69

DO - 10.1007/978-3-030-63820-7_69

M3 - Conference Paper

AN - SCOPUS:85097265871

SN - 9783030638191

T3 - Communications in Computer and Information Science

SP - 606

EP - 614

BT - Neural Information Processing

A2 - Yang, Haiqin

A2 - Pasupa, Kitsuchart

A2 - Leung, Andrew Chi-Sing

A2 - Kwok, James T.

A2 - Chan, Jonathan H.

A2 - King, Irwin

PB - Springer

CY - Cham Switzerland

T2 - International Conference on Neural Information Processing 2020

Y2 - 18 November 2020 through 22 November 2020

ER -

Harischandra N, Dharmaratne A , Cicuttini FM , Wang YY. Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images. In Yang H, Pasupa K, Leung ACS, Kwok JT, Chan JH, King I, editors, Neural Information Processing : 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part IV. Cham Switzerland: Springer. 2020. p. 606-614. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-63820-7_69

Voxel Classification Based Automatic Hip Cartilage Segmentation from Routine Clinical MR Images

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