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While state-of-the-art deep learning methods consistently provide the most accurate image segmentation results when sufficient training datasets are available, in some applications large datasets are difficult to acquire. For example, a model for inner ear structures can only be constructed by ex vivo specimen imaging modalities such as µCT. Constructing such datasets is costly and time consuming. Active shape models (ASM) have been a successful technique in medical image segmentation and require less extensive datasets for training. However, the ability of the ASM framework to capture complex shape variation is limited by representing variations across all global-pose-normalized training exemplars in a single, linear vector space. In this work, we describe a novel non-linear extension to the ASM in the form of a multi element ASMs. Instead of modelling shape from a single global pose as with the original ASM formulation, we capture differences in regional pose using a concept of multiple weighted ASMs which we call elements. Each element uses a unique set of landmark importance weights for use during the shape registration and model fitting process. Landmark weights are optimized to minimize the overall multi-element ASM’s fitting error on the training set shapes. We demonstrate the advantage of this approach in segmenting the labyrinth structure in the inner ear. We find that the multi element ASM consistently outperforms a traditional ASM on similar sample sizes, and multi-element ASMs trained on 10 samples outperform traditional ASMs trained with 15 samples. These results show the method’s potential advantages in applications that are limited by small shape libraries.
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