21 March 2016 2D image classification for 3D anatomy localization: employing deep convolutional neural networks
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Localization of anatomical regions of interest (ROIs) is a preprocessing step in many medical image analysis tasks. While trivial for humans, it is complex for automatic methods. Classic machine learning approaches require the challenge of hand crafting features to describe differences between ROIs and background. Deep convolutional neural networks (CNNs) alleviate this by automatically finding hierarchical feature representations from raw images. We employ this trait to detect anatomical ROIs in 2D image slices in order to localize them in 3D.

In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs — heart, aortic arch, and descending aorta. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it.

Classification performance of each CNN, expressed in area under the receiver operating characteristic curve, was ≥0.988. Additionally, the performance of ROI localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bob D. de Vos, Bob D. de Vos, Jelmer M. Wolterink, Jelmer M. Wolterink, Pim A. de Jong, Pim A. de Jong, Max A. Viergever, Max A. Viergever, Ivana Išgum, Ivana Išgum, } "2D image classification for 3D anatomy localization: employing deep convolutional neural networks", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841Y (21 March 2016); doi: 10.1117/12.2216971; https://doi.org/10.1117/12.2216971

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