A Multiscale-Based Adjustable Convolutional Neural Network for Multiple Organ Segmentation

Abstract

Accurate segmentation ofs organs-at-risk (OARs) in computed tomography (CT) is the key to planning treatment in radiation therapy (RT). Manually delineating OARs over hundreds of images of a typical CT scan can be time-consuming and error-prone. Deep convolutional neural networks with specific structures like U-Net have been proven effective for medical image segmentation. In this work, we propose an end-to-end deep neural network for multiorgan segmentation with higher accuracy and lower complexity. Compared with several state-of-the-art methods, the proposed accuracy-complexity adjustment module (ACAM) can increase segmentation accuracy and reduce the model complexity and memory usage simultaneously. An attention-based multiscale aggregation module (MAM) is also proposed for further improvement. Experiment results on chest CT datasets show that the proposed network achieves competitive Dice similarity coefficient results with fewer float-point operations (FLOPs) for multiple organs, which outperforms several state-of-the-art methods.

Automatic Semicircular Canal Segmentation of CT Volumes Using Improved 3D U‐Net with Attention Mechanism

“…We also compare the performance of our proposed network and the other state-of-the-art CNN architectures. The semicircular canal can be segmented effectively with the proposed method, so can the other organs in the future, for example, facial nerve [ 27 ], cochleae [ 28 ], and spinal cord [ 29 ]. …”

“…The semicircular canal can be segmented effectively with the proposed method, so can the other organs in the future, for example, facial nerve [ 27 ], cochleae [ 28 ], and spinal cord [ 29 ].…”