An anatomy-based iteratively searching convolutional neural network for organ localization in CT images

Tiange Liu; Meng Tan; Yubing Tong; Drew A. Torigian; Jayaram K. Udupa

doi:10.1117/12.2610963

4 April 2022 An anatomy-based iteratively searching convolutional neural network for organ localization in CT images

Tiange Liu, Meng Tan, Yubing Tong, Drew A. Torigian, Jayaram K. Udupa

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203227 (2022) https://doi.org/10.1117/12.2610963
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Organ localization is a common and essential preprocessing operation for many medical image analysis tasks. We propose a novel multi-organ localization method based on an end-to-end 3D convolutional neural network. The proposed algorithm employs a regression network to learn the position relationship between any patch and target organs in a medical computed tomography (CT) image. With this framework, it can iteratively localize the target organs in a coarse-to-fine manner. The main idea behind this method is to embed the anatomy of structures in a deep learning-based approach. For implementation, the proposed network outputs an 8-dimensional vector that contains information about the position, scale, and presence of each target organ. A piecewise loss function and a multi-density sampling strategy help to optimize this network to learn anatomy layout characteristics over the entire CT image. Starting from a random position, this network can accurately locate the target organ with a few iterations. Moreover, a dual-resolution strategy is employed to improve the accuracy affected by varying organ scales, further enhancing the localizing performance for all organs. We evaluate our method on a public data set (LiTS) to locate 11 organs in the thoraco-abdomino-pelvic region. The proposed method outperforms state-of-the-art methods with a mean intersection over union (IOU) of 80.84%, mean wall distance of 3.63 mm, and mean centroid distance of 4.93 mm, constituting excellent accuracy. The improvements on relatively small-size and medium-size organs are noteworthy

Citation Download Citation

Tiange Liu, Meng Tan, Yubing Tong, Drew A. Torigian, and Jayaram K. Udupa "An anatomy-based iteratively searching convolutional neural network for organ localization in CT images", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203227 (4 April 2022); https://doi.org/10.1117/12.2610963

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