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@inproceedings{1643616,
author = {Chmelik, Jiri and Jakubicek, Roman and Jan, Jiri and Ouředníček, Petr and Lambert, Lukas and Amadori, Elena and Gavelli, Giampaolo},
address = {NEW YORK},
booktitle = {WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 1},
doi = {http://dx.doi.org/10.1007/978-981-10-9035-6_28},
editor = {Lenka Lhotska; Lucie Sukupova; Igor Lacković; Geoffrey S. Ibbott},
keywords = {CAD; Convolution neural network; Spine analysis; Metastasis; CT data},
howpublished = {tištěná verze "print"},
language = {eng},
location = {NEW YORK},
isbn = {978-981-10-9034-9},
pages = {155-158},
publisher = {SPRINGER},
title = {Fully Automatic CAD System for Segmentation and Classification of Spinal Metastatic Lesions in CT Data},
url = {https://link.springer.com/chapter/10.1007/978-981-10-9035-6_28},
year = {2019}
}
TY - JOUR
ID - 1643616
AU - Chmelik, Jiri - Jakubicek, Roman - Jan, Jiri - Ouředníček, Petr - Lambert, Lukas - Amadori, Elena - Gavelli, Giampaolo
PY - 2019
TI - Fully Automatic CAD System for Segmentation and Classification of Spinal Metastatic Lesions in CT Data
PB - SPRINGER
CY - NEW YORK
SN - 9789811090349
KW - CAD
KW - Convolution neural network
KW - Spine analysis
KW - Metastasis
KW - CT data
UR - https://link.springer.com/chapter/10.1007/978-981-10-9035-6_28
L2 - https://link.springer.com/chapter/10.1007/978-981-10-9035-6_28
N2 - Our contribution presents a research progress in our long-term project that deals with spine analysis in computed tomography (CT) data. A fully automatic computer-aided diagnosis (CAD) system is presented, enabling the simultaneous segmentation and classification of metastatic tissues that can occur in the vertebrae of oncological patients. The task of the proposed CAD system is to segment metastatic lesions and classify them into two categories: osteolytic and osteoblastic. These lesions, especially osteolytic, are ill defined and it is difficult to detect them directly with only information about voxel intensity. The use of several local texture and shape features turned out to be useful for correct classification, however the exact determination of relevant image features is a difficult task. For this reason, the feature determination has been solved by automatic feature extraction provided by a deep convolutional neural network (CNN). The achieved mean sensitivity of detected lesions is greater than 92% with approximately three false positive detections per lesion for both types.
ER -
CHMELIK, Jiri, Roman JAKUBICEK, Jiri JAN, Petr OUŘEDNÍČEK, Lukas LAMBERT, Elena AMADORI and Giampaolo GAVELLI. Fully Automatic CAD System for Segmentation and Classification of Spinal Metastatic Lesions in CT Data. In Lenka Lhotska; Lucie Sukupova; Igor Lackovi\'c; Geoffrey S. Ibbott. \textit{WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 1}. NEW YORK: SPRINGER, 2019, p.~155-158. ISBN~978-981-10-9034-9. Available from: https://dx.doi.org/10.1007/978-981-10-9035-6\_{}28.
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