MATLAB PROJECT
Efficient Multiple
Organ Localization in CT Image Using 3D Region Proposal Network
Abstract:
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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