As an essential prerequisite for developing a medical intelligent assistant system, medical image segmentation has received extensive research and concentration from the neural network community. A series of UNet-like networks with encoder-decoder architecture has achieved extraordinary success, in which UNet2+ and UNet3+ redesign skip connections, respectively proposing dense skip connection and full-scale skip connection and dramatically improving compared with UNet in medical image segmentation. However, UNet2+ lacks sufficient information explored from the full scale, which will affect the learning of organs' location and boundary. Although UNet3+ can obtain the full-scale aggregation feature map, owing to the small number of neurons in the structure, it does not satisfy the segmentation of tiny objects when the number of samples is small. This paper proposes a novel network structure combining dense skip connections and full-scale skip connections, named UNet-sharp (UNet\#) for its shape similar to symbol \#. The proposed UNet\# can aggregate feature maps of different scales in the decoder sub-network and capture fine-grained details and coarse-grained semantics from the full scale, which benefits learning the exact location and accurately segmenting the boundary of organs or lesions. We perform deep supervision for model pruning to speed up testing and make it possible for the model to run on mobile devices; furthermore, designing two classification-guided modules to reduce false positives achieves more accurate segmentation results. Various experiments of semantic segmentation and instance segmentation on different modalities (EM, CT, MRI) and dimensions (2D, 3D) datasets, including the nuclei, brain tumor, liver, and lung, demonstrate that the proposed method outperforms state-of-the-art models.
翻译:作为开发医学智能助理系统的基本先决条件,医学图像分割作为发展医学智能助手系统的基本前提,已经从神经网络界获得了广泛的研究和集中。一系列类似UNetet的网络,其编码解码器-解码器结构已经取得了非凡的成功,其中UNet2+和UNet3+重新设计跳过连接,分别提出密集跳过连接和全面跳过连接,并与UNet在医学图像分割中与UNet相比有了显著的改善。然而,UNet2+缺乏从整个规模中探索的足够信息,这将影响器官位置和边界的学习。尽管UNet3+能够获得全面的聚合特征图,由于结构中神经部分数量少,因此无法满足小物体的分解。在样本数量小的时候,UNet2+和UNet3+重新设计连接,将密集跳过连接和全面跳过连接,并大大改进与UNet+在医学图像分割中的位置。拟议的UNet+可以将不同比例的模型显示在解码器子网络中的缩略图,并捕捉取精度和粗略的汇总组合特征特征图,因为结构结构在结构结构中数量小时不能满足。 进行精确的模型和深层次分析,我们可以学习定位,从而测试运行到深层段,并测试。