Abstract
Accurate automatic Identification and localization of spine vertebrae points in CT scan images is crucial in medical diagnosis. This paper presents an automatic feature extraction network, based on transfer learned CNN, in order to handle the availability of limited samples. The 3D vertebrae centroids are identified and localized by an LSTM network, which is trained on CNN features extracted from 242 CT spine sequences. The model is further trained to estimate age and gender from LSTM features. Thus, we present a framework that serves as a multi-task data driven model for identifying and localizing spine vertebrae points, age estimation and gender classification. The proposed approach is compared with benchmark results obtained by testing 60 scans. The advantage of the multi-task framework is that it does not need any additional information other than the annotations on the spine images indicating the presence of vertebrae points.
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Harini, N. et al. (2021). Multi-task Data Driven Modelling Based on Transfer Learned Features in Deep Learning for Biomedical Application. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 171. Springer, Singapore. https://doi.org/10.1007/978-981-33-4543-0_20
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DOI: https://doi.org/10.1007/978-981-33-4543-0_20
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