Deep hybrid neural-like P systems for multiorgan segmentation in head and neck CT/MR images

Automatic segmentation of organs-at-risk (OARs) of the head and neck, such as the brainstem, the left and right parotid glands, mandible, optic chiasm, and the left and right optic nerves, are crucial when formulating radiotherapy plans. However, there are difficulties due to (1) the small sizes of these organs (especially the optic chiasm and optic nerves) and (2) the different positions and phenotypes of the OARs. In this paper, we propose a novel, automatic multiorgan segmentation algorithm based on a new hybrid neural-like P system, to alleviate the above challenges. The new P system possesses the joint advantages of cell-like and neural-like P systems and includes new structures and rules, allowing it to solve more real-world problems in parallelism. In the new P system, effective ensemble convolutional neural networks (CNNs) are implemented with different initializations simultaneously to perform pixel-wise segmentations of OARs, which can obtain more effective features and leverage the strength of ensemble learning. Evaluations on three public datasets show the effectiveness and robustness of the proposed algorithm for accurate OARs segmentation in various image modalities.

Automated summary

This paper proposes an automatic multiorgan segmentation algorithm based on a new hybrid neural-like P system, which can alleviate challenges in segmenting organs-at-risk (OARs) of the head and neck. The algorithm uses effective ensemble convolutional neural networks (CNNs) for pixel-wise segmentation, showing effectiveness and robustness in accurate OARs segmentation across various image modalities.