论文标题
MNET：重新思考各向异性医学图像分割的2D/3D网络
MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation
论文作者
论文摘要
厚切片扫描的性质引起了3D医学图像的严重固定间歇性，而Vanilla 2D/3D卷积神经网络（CNNS）无法代表稀疏的切片间信息，并以平衡的方式以平衡的方式来表现出稀疏的切片间信息，从而导致了与Vanilla 2D Snlas的频率（For Vanilla Snns）的严重下降（导致Vanilla 2D dd dd dd dd dd dd dd dd dd） 3D CNN）。在这项工作中，提出了一个新颖的网络网络（MNET），以通过学习来平衡空间表示。 1）我们的MNET通过将多维卷积嵌入深度融合到基本模块中，从而融合了大量表示过程，从而使表示过程的选择灵活，从而平衡了稀疏片间信息和密集的内部内部内部玻璃内部信息。 2）我们的MNET融合了每个基本模块内部的多维特征，同时采用了2D（2D视图中易于识别区域的高分割精度）和3D（3D器官轮廓的高平滑度）的优点，从而获得了目标区域的更准确的建模。全面的实验是在四个公共数据集（CT \＆MR）上进行的，结果始终证明所提出的MNET优于其他方法。代码和数据集可在以下网址找到：https：//github.com/zfdong-code/mnet
The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and overfitting to noise from long-range slices (for vanilla 3D CNNs). In this work, a novel mesh network (MNet) is proposed to balance the spatial representation inter axes via learning. 1) Our MNet latently fuses plenty of representation processes by embedding multi-dimensional convolutions deeply into basic modules, making the selections of representation processes flexible, thus balancing representation for sparse inter-slice information and dense intra-slice information adaptively. 2) Our MNet latently fuses multi-dimensional features inside each basic module, simultaneously taking the advantages of 2D (high segmentation accuracy of the easily recognized regions in 2D view) and 3D (high smoothness of 3D organ contour) representations, thus obtaining more accurate modeling for target regions. Comprehensive experiments are performed on four public datasets (CT\&MR), the results consistently demonstrate the proposed MNet outperforms the other methods. The code and datasets are available at: https://github.com/zfdong-code/MNet
