Shape modeling of volumetric medical images is crucial for quantitative analysis and surgical planning in computer-aided diagnosis. To alleviate the burden of expert clinicians, reconstructed shapes are typically obtained from deep learning models, such as Convolutional Neural Networks (CNNs) or transformer-based architectures, followed by the marching cube algorithm. However, automatic shape reconstruction often falls short of perfection due to the limited resolution of images and the absence of shape prior constraints. To overcome these limitations, we propose the Reliable Shape Interaction with Implicit Template (ReShapeIT) network, which models anatomical structures in continuous space rather than discrete voxel grids. ReShapeIT represents an anatomical structure with an implicit template field shared within the same category, complemented by a deformation field. It ensures the implicit template field generates valid templates by strengthening the constraint of the correspondence between the instance shape and the template shape. The valid template shape can then be utilized for implicit generalization. A Template Interaction Module (TIM) is introduced to reconstruct unseen shapes by interacting the valid template shapes with the instance-wise latent codes. Experimental results on three datasets demonstrate the superiority of our approach in anatomical structure reconstruction. The Chamfer Distance/Earth Mover's Distance achieved by ReShapeIT are 0.225/0.318 on Liver, 0.125/0.067 on Pancreas, and 0.414/0.098 on Lung Lobe.

翻译：体积医学图像的形状建模对于计算机辅助诊断中的定量分析和手术规划至关重要。为减轻临床专家的负担，重建形状通常通过深度学习模型（如卷积神经网络（CNN）或基于Transformer的架构）结合行进立方体算法获得。然而，由于图像分辨率有限以及缺乏形状先验约束，自动形状重建往往难以达到完美。为克服这些限制，我们提出了基于隐式模板的可靠形状交互（ReShapeIT）网络，该网络在连续空间而非离散体素网格中对解剖结构进行建模。ReShapeIT使用同一类别内共享的隐式模板场以及一个形变场来表示解剖结构。它通过加强实例形状与模板形状之间对应关系的约束，确保隐式模板场生成有效的模板。随后，有效的模板形状可用于隐式泛化。我们引入了模板交互模块（TIM），通过将有效模板形状与实例特定的隐码进行交互，以重建未见过的形状。在三个数据集上的实验结果表明，我们的方法在解剖结构重建方面具有优越性。ReShapeIT在肝脏、胰腺和肺叶数据集上取得的倒角距离/推土机距离分别为0.225/0.318、0.125/0.067和0.414/0.098。