Automatic metastatic bone tumor classification with DCNN-based features using treatment-planning CT

Haruna Watanabe; Ren Togo; Takahiro Ogawa; Miki Haseyama; Koichi Yasuda; Khin Khin Tha; Kohsuke Kudo; Hiroki Shirato

doi:10.1117/12.2521406

27 March 2019 Automatic metastatic bone tumor classification with DCNN-based features using treatment-planning CT

Haruna Watanabe, Ren Togo, Takahiro Ogawa, Miki Haseyama, Koichi Yasuda, Khin Khin Tha, Kohsuke Kudo, Hiroki Shirato

Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110501A (2019) https://doi.org/10.1117/12.2521406
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

In this paper, we propose a method to classify metastatic bone tumors using treatment-planning computed tomography images. The proposed method utilizes pre-trained deep convolutional neural network (DCNN) models as feature extractors and enables the metastatic bone tumor classification by using the obtained features. Performance of several state-of-the-art DCNN-based features was compared and evaluated in our experiment.

Citation Download Citation

Haruna Watanabe, Ren Togo, Takahiro Ogawa, Miki Haseyama, Koichi Yasuda, Khin Khin Tha, Kohsuke Kudo, and Hiroki Shirato "Automatic metastatic bone tumor classification with DCNN-based features using treatment-planning CT", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110501A (27 March 2019); https://doi.org/10.1117/12.2521406

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