Detail publikačního výsledku

Fully Automatic CAD System for Segmentation and Classification of Spinal Metastatic Lesions in CT Data

CHMELÍK, J.; JAKUBÍČEK, R.; JAN, J.; OUŘEDNÍČEK, P.; LAMBERT, L.; AMADORI, E.; GAVELLI, G.

Originální název

Fully Automatic CAD System for Segmentation and Classification of Spinal Metastatic Lesions in CT Data

Anglický název

Fully Automatic CAD System for Segmentation and Classification of Spinal Metastatic Lesions in CT Data

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

CAD; Convolution neural network; Spine analysis; Metastasis; CT data

Klíčová slova v angličtině

CAD; Convolution neural network; Spine analysis; Metastasis; CT data

Autoři

CHMELÍK, J.; JAKUBÍČEK, R.; JAN, J.; OUŘEDNÍČEK, P.; LAMBERT, L.; AMADORI, E.; GAVELLI, G.

Rok RIV

2019

Vydáno

02.01.2019

Nakladatel

Springer

Místo

Singapore

ISBN

978-981-10-9034-9

Kniha

World Congress on Medical Physics and Biomedical Engineering 2018

ISSN

1680-0737

Periodikum

IFMBE Proceedings

Svazek

68

Číslo

1

Stát

Francouzská republika

Strany od

155

Strany do

158

Strany počet

4

URL

https://doi.org/10.1007/978-981-10-9035-6_28

BibTex

@inproceedings{BUT147831,
  author="CHMELÍK, J. and JAKUBÍČEK, R. and JAN, J. and OUŘEDNÍČEK, P. and LAMBERT, L. and AMADORI, E. and GAVELLI, G.",
  title="Fully Automatic CAD System for Segmentation and Classification of Spinal Metastatic Lesions in CT Data",
  booktitle="World Congress on Medical Physics and Biomedical Engineering 2018",
  year="2019",
  journal="IFMBE Proceedings",
  volume="68",
  number="1",
  pages="155--158",
  publisher="Springer",
  address="Singapore",
  doi="10.1007/978-981-10-9035-6\{_}28",
  isbn="978-981-10-9034-9",
  issn="1680-0737",
  url="https://doi.org/10.1007/978-981-10-9035-6_28"
}

Dokumenty

465677_1_En_28_Chapter_Author