TY - GEN
T1 - A radiomics approach to traumatic brain injury prediction in CT scans
AU - Rosa, Ezequiel De La
AU - Sima, Diana M.
AU - Vyvere, Thijs Vande
AU - Kirschke, Jan S.
AU - Menze, Bjoern
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Computer Tomography (CT) is the gold standard technique for brain damage evaluation after acute Traumatic Brain Injury (TBI). It allows identification of most lesion types and determines the need of surgical or alternative therapeutic procedures. However, the traditional approach for lesion classification is restricted to visual image inspection. In this work, we characterize and predict TBI lesions by using CT-derived radiomics descriptors. Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial Least Squares. On a dataset containing 155 scans (105 train, 50 test) the methodology achieved 89.7% accuracy over the unseen data. When a model was built using only texture features, a 88.2% accuracy was obtained. Our results suggest that selected radiomics descriptors could play a key role in brain injury prediction. Besides, the proposed methodology is close to reproduce radiologists lesion labelling. These results open new possibilities for radiomics-inspired brain lesion detection, segmentation and prediction.
AB - Computer Tomography (CT) is the gold standard technique for brain damage evaluation after acute Traumatic Brain Injury (TBI). It allows identification of most lesion types and determines the need of surgical or alternative therapeutic procedures. However, the traditional approach for lesion classification is restricted to visual image inspection. In this work, we characterize and predict TBI lesions by using CT-derived radiomics descriptors. Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial Least Squares. On a dataset containing 155 scans (105 train, 50 test) the methodology achieved 89.7% accuracy over the unseen data. When a model was built using only texture features, a 88.2% accuracy was obtained. Our results suggest that selected radiomics descriptors could play a key role in brain injury prediction. Besides, the proposed methodology is close to reproduce radiologists lesion labelling. These results open new possibilities for radiomics-inspired brain lesion detection, segmentation and prediction.
KW - Ct
KW - Radiomics
KW - Traumatic brain injury
UR - http://www.scopus.com/inward/record.url?scp=85073911038&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2019.8759229
DO - 10.1109/ISBI.2019.8759229
M3 - Conference contribution
AN - SCOPUS:85073911038
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 732
EP - 735
BT - ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Y2 - 8 April 2019 through 11 April 2019
ER -