College of Mathematics and Physics, Wenzhou University, Wenzhou, 325035, Zhejiang, China.
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
Comput Methods Programs Biomed. 2024 Jan;243:107885. doi: 10.1016/j.cmpb.2023.107885. Epub 2023 Oct 27.
Medical image segmentation has garnered significant research attention in the neural network community as a fundamental requirement for developing intelligent medical assistant systems. A series of UNet-like networks with an encoder-decoder architecture have achieved remarkable success in medical image segmentation. Among these networks, UNet2+ (UNet++) and UNet3+ (UNet+++) have introduced redesigned skip connections, dense skip connections, and full-scale skip connections, respectively, surpassing the performance of the original UNet. However, UNet2+ lacks comprehensive information obtained from the entire scale, which hampers its ability to learn organ placement and boundaries. Similarly, due to the limited number of neurons in its structure, UNet3+ fails to effectively segment small objects when trained with a small number of samples.
In this study, we propose UNet_sharp (UNet#), a novel network topology named after the "#" symbol, which combines dense skip connections and full-scale skip connections. In the decoder sub-network, UNet# can effectively integrate feature maps of different scales and capture fine-grained features and coarse-grained semantics from the entire scale. This approach enhances the understanding of organ and lesion positions and enables accurate boundary segmentation. We employ deep supervision for model pruning to accelerate testing and enable mobile device deployment. Additionally, we construct two classification-guided modules to reduce false positives and improve segmentation accuracy.
Compared to current UNet-like networks, our proposed method achieves the highest Intersection over Union (IoU) values ((92.67±0.96)%, (92.38±1.29)%, (95.36±1.22)%, (74.01±2.03)%) and F1 scores ((91.64±1.86)%, (95.70±2.16)%, (97.34±2.76)%, (84.77±2.65)%) on the semantic segmentation tasks of nuclei, brain tumors, liver, and lung nodules, respectively.
The experimental results demonstrate that the reconstructed skip connections in UNet successfully incorporate multi-scale contextual semantic information. Compared to most state-of-the-art medical image segmentation models, our proposed method more accurately locates organs and lesions and precisely segments boundaries.
医学图像分割是神经网络领域的一个研究热点，是开发智能医疗辅助系统的基础。一系列具有编码器-解码器结构的 UNet 类网络在医学图像分割中取得了显著的成功。在这些网络中，UNet2+（UNet++）和 UNet3+（UNet+++）分别引入了重新设计的跳跃连接、密集跳跃连接和全尺度跳跃连接，超越了原始 UNet 的性能。然而，UNet2+缺乏来自整个尺度的全面信息，这限制了它学习器官位置和边界的能力。同样，由于其结构中的神经元数量有限，当用少量样本进行训练时，UNet3+无法有效地分割小物体。
在这项研究中，我们提出了 UNet_sharp（UNet#），这是一种以“#”符号命名的新网络拓扑，它结合了密集跳跃连接和全尺度跳跃连接。在解码器子网络中，UNet#可以有效地整合不同尺度的特征图，并从整个尺度捕获精细的特征和粗粒度的语义。这种方法增强了对器官和病变位置的理解，并能够实现精确的边界分割。我们采用深度监督进行模型剪枝，以加速测试并实现移动设备部署。此外，我们构建了两个分类引导模块，以减少假阳性并提高分割准确性。
与现有的 UNet 类网络相比，我们的方法在核、脑肿瘤、肝和肺结节的语义分割任务上分别取得了最高的交并比（IoU）值（（92.67±0.96）%，（92.38±1.29）%，（95.36±1.22）%，（74.01±2.03）%）和 F1 分数（（91.64±1.86）%，（95.70±2.16）%，（97.34±2.76）%，（84.77±2.65）%）。
实验结果表明，UNet 中的重建跳跃连接成功地结合了多尺度上下文语义信息。与大多数最先进的医学图像分割模型相比，我们的方法更准确地定位器官和病变，并精确地分割边界。
