Detail publikace

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

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

Jazyk

en

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.

BibTex


@inproceedings{BUT147831,
  author="Jiří {Chmelík} and Roman {Jakubíček} and Jiří {Jan} and Petr {Ouředníček} and Lukáš {Lambert} and Elena {Amadori} and Giampaolo {Gavelli}",
  title="Fully Automatic CAD System for Segmentation and Classification of Spinal Metastatic Lesions in CT Data",
  annote="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.",
  address="Springer",
  booktitle="World Congress on Medical Physics and Biomedical Engineering 2018",
  chapter="147831",
  doi="10.1007/978-981-10-9035-6_28",
  howpublished="print",
  institution="Springer",
  number="1",
  year="2018",
  month="may",
  pages="155--158",
  publisher="Springer",
  type="conference paper"
}