An Anatomy-based Iteratively Searching Convolutional Neural Network for Organ Localization in CT images

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.