Medical image segmentation is important for disease diagnosis and support medical decision systems. The study proposes an efficient 3D semantic segmentation deep learning model “3D-DenseUNet-569” for liver and tumor segmentation. The proposed 3D-DenseUNet-569 is a fully 3D semantic segmentation model with a significantly deeper network and lower trainable parameters. The proposed model adopts Depthwise Separable Convolution (DS-Conv) as opposed to traditional convolution. The DS-Conv significantly decreases GPU memory requirements and computational cost and achieves high performance. The proposed 3D-DenseUNet-569 utilizes DensNet connections and UNet links, which preserve low-level features and produce effective results. The results of experimental study on the standard LiTS dataset demonstrate that the 3D-DenseNet-569 model is effective and efficient with respect to related studies.

医学图像分割对于疾病诊断和支持医学决策系统很重要。该研究提出了一种有效的3D语义分割深度学习模型“ 3D-DenseUNet-569”，用于肝脏和肿瘤分割。提出的3D-DenseUNet-569是具有明显更深的网络和更低的可训练参数的全3D语义分割模型。所提出的模型采用深度可分离卷积（DS-Conv），而不是传统卷积。DS-Conv大大降低了GPU内存需求和计算成本，并实现了高性能。拟议的3D-DenseUNet-569利用DensNet连接和UNet链接来保留低级功能并产生有效结果。