Towards automatic 3D bone marrow segmentation

Chuong T. Nguyen; Joseph P. Havlicek; Jennifer Holter Chakrabarty; Quyen Duong; Sara K. Vesely

doi:10.1109/SSIAI.2016.7459162

Towards automatic 3D bone marrow segmentation

Chuong T. Nguyen, Joseph P. Havlicek, Jennifer Holter Chakrabarty, Quyen Duong, Sara K. Vesely

Quantitative Health Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Current noninvasive evaluation of bone marrow proliferation in leukemia treatment is limited to manually examining marrow tissue in multiple regions of interest (ROIs). The statistics extracted from these ROIs often fail to provide an accurate global characterization of the patient's marrow. We propose an automatic framework for segmenting spinal marrow compartments to characterize the bone marrow from full-body joint PET/CT scans acquired subsequent to bone marrow transplantation. We first apply a graph-cut algorithm to the CT volume to obtain a 3D full-body bone map. We then isolate the spinal column in a single sagittal plane where connected components labeling and iterative thresholding are used to segment the vertebral bodies. This fully automated approach achieves an average accuracy of 91.7% and a worst case accuracy of 80.4% in testing on 51 scans of 17 patients. Finally, we outline a method for rejecting the cortical bone in transverse planes that can be combined with the sagittally segmented vertebral bodies to obtain a 3D map of the vertebral body medullary cavities for the entire spine.

Original language	English (US)
Title of host publication	2016 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	9-12
Number of pages	4
ISBN (Electronic)	9781467399197
DOIs	https://doi.org/10.1109/SSIAI.2016.7459162
State	Published - Apr 25 2016
Event	IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Santa Fe, United States Duration: Mar 6 2016 → Mar 8 2016

Publication series

Name	Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
Volume	2016-April

Conference

Conference	IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016
Country/Territory	United States
City	Santa Fe
Period	3/6/16 → 3/8/16

Keywords

3D bone segmentation
bone marrow extraction
PET/CT
SUV

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Computer Science Applications

Access to Document

10.1109/SSIAI.2016.7459162

Cite this

Nguyen, C. T., Havlicek, J. P., Chakrabarty, J. H., Duong, Q., & Vesely, S. K. (2016). Towards automatic 3D bone marrow segmentation. In 2016 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Proceedings (pp. 9-12). [7459162] (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation; Vol. 2016-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSIAI.2016.7459162

Towards automatic 3D bone marrow segmentation. / Nguyen, Chuong T.; Havlicek, Joseph P.; Chakrabarty, Jennifer Holter et al.

2016 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 9-12 7459162 (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation; Vol. 2016-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nguyen, CT, Havlicek, JP, Chakrabarty, JH, Duong, Q & Vesely, SK 2016, Towards automatic 3D bone marrow segmentation. in 2016 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Proceedings., 7459162, Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, vol. 2016-April, Institute of Electrical and Electronics Engineers Inc., pp. 9-12, IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016, Santa Fe, United States, 3/6/16. https://doi.org/10.1109/SSIAI.2016.7459162

Nguyen CT, Havlicek JP, Chakrabarty JH, Duong Q, Vesely SK. Towards automatic 3D bone marrow segmentation. In 2016 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 9-12. 7459162. (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation). doi: 10.1109/SSIAI.2016.7459162

Nguyen, Chuong T. ; Havlicek, Joseph P. ; Chakrabarty, Jennifer Holter et al. / Towards automatic 3D bone marrow segmentation. 2016 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 9-12 (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation).

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abstract = "Current noninvasive evaluation of bone marrow proliferation in leukemia treatment is limited to manually examining marrow tissue in multiple regions of interest (ROIs). The statistics extracted from these ROIs often fail to provide an accurate global characterization of the patient's marrow. We propose an automatic framework for segmenting spinal marrow compartments to characterize the bone marrow from full-body joint PET/CT scans acquired subsequent to bone marrow transplantation. We first apply a graph-cut algorithm to the CT volume to obtain a 3D full-body bone map. We then isolate the spinal column in a single sagittal plane where connected components labeling and iterative thresholding are used to segment the vertebral bodies. This fully automated approach achieves an average accuracy of 91.7% and a worst case accuracy of 80.4% in testing on 51 scans of 17 patients. Finally, we outline a method for rejecting the cortical bone in transverse planes that can be combined with the sagittally segmented vertebral bodies to obtain a 3D map of the vertebral body medullary cavities for the entire spine.",

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AB - Current noninvasive evaluation of bone marrow proliferation in leukemia treatment is limited to manually examining marrow tissue in multiple regions of interest (ROIs). The statistics extracted from these ROIs often fail to provide an accurate global characterization of the patient's marrow. We propose an automatic framework for segmenting spinal marrow compartments to characterize the bone marrow from full-body joint PET/CT scans acquired subsequent to bone marrow transplantation. We first apply a graph-cut algorithm to the CT volume to obtain a 3D full-body bone map. We then isolate the spinal column in a single sagittal plane where connected components labeling and iterative thresholding are used to segment the vertebral bodies. This fully automated approach achieves an average accuracy of 91.7% and a worst case accuracy of 80.4% in testing on 51 scans of 17 patients. Finally, we outline a method for rejecting the cortical bone in transverse planes that can be combined with the sagittally segmented vertebral bodies to obtain a 3D map of the vertebral body medullary cavities for the entire spine.

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Towards automatic 3D bone marrow segmentation

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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