&lt;title&gt;Streamlined volumetric landmark placement method for building three-dimensional active shape models&lt;/title&gt;

We propose a novel fully convolutional network architecture for shapes, denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images. Meanwhile, to build our SFCN architecture in the original image segmentation fully convolutional network (FCN) architecture, we also design and implement a generating operation with bridging function. This ensures that the convolution and pooling operation we have designed can be successfully applied in the original FCN architecture. In this paper, we also present a new shape segmentation approach based on SFCN. Furthermore, we allow more general and challenging input, such as mixed datasets of different categories of shapes which can prove the ability of our generalisation. In our approach, SFCNs are trained triangles-to-triangles by using three low-level geometric features as input. Finally, the feature voting-based multi-label graph cuts is adopted to optimise the segmentation results obtained by SFCN prediction. The experiment results show that our method can effectively learn and predict mixed shape datasets of either similar or different characteristics, and achieve excellent segmentation results.

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Volumetric segmentation via 3D active shape models

Dickens

Gleason

Sari‐Sarraf

Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation

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<title>Streamlined volumetric landmark placement method for building three-dimensional active shape models</title>

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Volumetric segmentation via 3D active shape models

Volumetric segmentation via 3D active shape models

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