A 3D self-adjustable organ aware deep network for abdominal segmentation in CT images
Shenhai Zheng, Haiguo Zhao, Hong Wang, Laquan Li · 2023
CT scan is an important reference means of disease diagnosis. Automatic segmentation of organ regions can save a lot of time and labor costs, and allow doctors to produce more intuitive observations of the internal organization of the human body structure. However, automatic multi-organ segmentation remains challenging due to the complicated anatomical structures and low tissue contrast in CT images. Traditional segmentation methods are relatively inefficient for organ segmentation and the traditional network architectures are rarely designed to meet the requirements of lightweight and efficient clinical practice. In this paper, we propose a novel network named Self-Adjustable Organ Attention U-Net (SOA-Net) to overcome these limitations. The SOA-Net includes multi-branches feature attention (MBFA) module and the feature attention aggregation (FAA) module. These two modules have multiple branches with different kernel sizes to adaptively adjust their receptive field size based on multiple scales of the target organs. The SOA-Net is a 3D self-adjustable organ aware deep network which can adaptively adjust receptive fields sizes based on multiple scales of the target organs to realize the efficient segmentation of multiple abdominal organs. We evaluate our method on a challenging abdominal multi-organ CT dataset, and the final experiments proved that our model achieves the best segmentation performance compared with the state-of-the-art segmentation networks.