In recent years, deep learning has been successfully applied to medical image segmentation. However, as the network extends deeper, the consecutive downsampling operations will lead to more loss of spatial information. In addition, the limited data and diverse targets increase the difficulty for medical image segmentation. To address these issues, we propose a multi-path connected network (MCNet) for medical segmentation problems. It integrates multiple paths generated by pyramid pooling into the encoding phase to preserve semantic information and spatial details. We utilize multi-scale feature extractor block (MFE block) in the encoder to obtain large and multi-scale receptive fields. We evaluated MCNet on three medical datasets with different image modalities. The experimental results show that our method achieves better performance than the state-of-the-art approaches. Our model has strong feature learning ability and is robust to capture different scale targets. It can achieve satisfactory results while using only 0.98 million (M) parameters.

近年来，深度学习已成功地应用于医学图像分割。但是，随着网络的扩展，连续的下采样操作将导致更多的空间信息丢失。此外，有限的数据和多样化的目标增加了医学图像分割的难度。为了解决这些问题，我们提出了一种用于医疗细分问题的多路径连接网络（MCNet）。它将金字塔池生成的多个路径集成到编码阶段，以保留语义信息和空间细节。我们利用编码器中的多尺度特征提取器块（MFE块）来获得大尺度和多尺度的接收场。我们在具有不同图像模态的三个医学数据集上评估了MCNet。实验结果表明，我们的方法比最新方法具有更好的性能。我们的模型具有强大的特征学习能力，并且能够捕获不同规模的目标。仅使用98万（M）参数，即可获得令人满意的结果。