This work demonstrates a full reproduction and extension of MNet, a hybrid 2D/3D convolutional network designed for anisotropic medical image segmentation. The original architecture was re-implemented within the nnU-Net framework to verify its reported performance and robustness to variable voxel spacing, known as anisotropy. Experiments were conducted on PROMISE prostate MRI and a controlled subset of LiTS liver CT under matched preprocessing and compute constraints. The reproduced MNet achieved a Dice similarity coefficient (DSC) of 89.0 +/- 0.9% on PROMISE, within 0.8% of the published result, and 94.3 +/- 1.9% / 54.6 +/- 3.1% for liver and tumor segmentation on LiTS, respectively. Two lightweight extensions were further introduced: (1) a learned Fusion Gating mechanism enabling adaptive 2D-3D feature blending, and (2) a VMamba state-space module for efficient long-range depth modelling. The Spatial Gating variant improved DSC by +0.8% with less than 3% inference overhead, while VMamba improved performance consistency, reducing PROMISE Dice variation to +/- 0.7% and achieving the strongest LiTS liver performance at 95.8% Dice. Both extensions preserved MNet robustness to anisotropy, with delta Dice = 1.5% across 1-4 mm voxel spacing. Overall, the study confirms MNet reproducibility and demonstrates that adaptive fusion and state-space modelling have the potential to further strengthen segmentation reliability under anisotropic conditions. However, further tests are required to provide definitive conclusions.

翻译：本工作完整复现并扩展了MNet——一种专为各向异性医学图像分割设计的混合2D/3D卷积网络。我们在nnU-Net框架内重新实现了原始架构，以验证其报告的性能及其对不同体素间距（即各向异性）的鲁棒性。在匹配的预处理和计算约束条件下，在PROMISE前列腺MRI数据集和LiTS肝脏CT数据集的受控子集上进行了实验。复现的MNet在PROMISE数据集上达到了89.0 +/- 0.9%的Dice相似系数（DSC），与已发表结果相差0.8%以内；在LiTS数据集上肝脏和肿瘤分割分别达到了94.3 +/- 1.9%和54.6 +/- 3.1%。我们还引入了两种轻量级扩展：（1）一种学习型融合门控机制，实现自适应2D-3D特征混合；（2）一个VMamba状态空间模块，用于高效的长程深度建模。空间门控变体将DSC提升了+0.8%，且推理开销低于3%；而VMamba提高了性能一致性，将PROMISE Dice变异降至+/- 0.7%，并取得了最强的LiTS肝脏分割性能（Dice为95.8%）。两种扩展均保持了MNet对各向异性的鲁棒性，在体素间距1-4 mm范围内的Dice变化（delta Dice）为1.5%。总体而言，本研究证实了MNet的可复现性，并表明自适应融合与状态空间建模有潜力进一步强化各向异性条件下的分割可靠性。然而，仍需进一步测试以得出确切结论。