TY - GEN
T1 - An automatic 3D CT/PET segmentation framework for bone marrow proliferation assessment
AU - Nguyen, Chuong
AU - Havlicek, Joseph
AU - Duong, Quyen
AU - Vesely, Sara
AU - Gress, Ronald
AU - Lindenberg, Liza
AU - Choyke, Peter
AU - Chakrabarty, Jennifer Holter
AU - Williams, Kirsten
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/3
Y1 - 2016/8/3
N2 - Clinical assessment of bone marrow is limited by an inability to evaluate the marrow space comprehensively and dynamically and there is no current method for automatically assessing hematopoietic activity within the medullary space. Evaluating the hematopoietic space in its entirety could be applicable in blood disorders, malignancies, infections, and medication toxicity. In this paper, we introduce a CT/PET 3D automatic framework for measurement of the hematopoietic compartment proliferation within osseous sites. We first perform a full-body bone structure segmentation using 3D graph-cut on the CT volume. The vertebrae are segmented by detecting the discs between adjacent vertebrae. Finally, we register the bone marrow CT volume with its corresponding PET volume and capture the spinal bone marrow volume. The proposed framework was tested on 17 patients, achieving an average accuracy of 86.37% and a worst case accuracy of 82.3% in automatically extracting the aggregate volume of the spinal marrow cavities.
AB - Clinical assessment of bone marrow is limited by an inability to evaluate the marrow space comprehensively and dynamically and there is no current method for automatically assessing hematopoietic activity within the medullary space. Evaluating the hematopoietic space in its entirety could be applicable in blood disorders, malignancies, infections, and medication toxicity. In this paper, we introduce a CT/PET 3D automatic framework for measurement of the hematopoietic compartment proliferation within osseous sites. We first perform a full-body bone structure segmentation using 3D graph-cut on the CT volume. The vertebrae are segmented by detecting the discs between adjacent vertebrae. Finally, we register the bone marrow CT volume with its corresponding PET volume and capture the spinal bone marrow volume. The proposed framework was tested on 17 patients, achieving an average accuracy of 86.37% and a worst case accuracy of 82.3% in automatically extracting the aggregate volume of the spinal marrow cavities.
KW - Bone marrow extraction
KW - Bone segmentation
KW - CT/PET imaging
UR - http://www.scopus.com/inward/record.url?scp=85006757692&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006757692&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2016.7533136
DO - 10.1109/ICIP.2016.7533136
M3 - Conference contribution
AN - SCOPUS:85006757692
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 4126
EP - 4130
BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PB - IEEE Computer Society
T2 - 23rd IEEE International Conference on Image Processing, ICIP 2016
Y2 - 25 September 2016 through 28 September 2016
ER -