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Efficient Multiple Organ Localization in CT Image Using 3D Region Proposal Network

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Organ localization is an essential preprocessing step for many medical image analysis tasks, such as image registration, organ segmentation, and lesion detection. In this paper, we propose an efficient method for multiple organ localization in CT image using a 3D region proposal network. Compared with other convolutional neural network-based methods that successively detect the target organs in all slices to assemble the final 3D bounding box, our method is fully implemented in a 3D manner, and thus, it can take full advantages of the spatial context information in CT image to perform efficient organ localization with only one prediction. We also propose a novel backbone network architecture that generates high-resolution feature maps to further improve the localization performance on small organs. We evaluate our method on two clinical datasets, where 11 body organs and 12 head organs (or anatomical structures) are included. As our results shown, the proposed method achieves higher detection precision and localization accuracy than the current state-of-the-art methods with approximate 4 to 18 times faster processing speed. Additionally, we have established a public dataset dedicated for organ localization on http://dx.doi.org/10.21227/df8g-pq27. The full implementation of the proposed method has also been made publicly available on https://github.com/superxuang/caffe_3d_faster_rcnn.

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