25 research outputs found
End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation
Automatic segmentation of abdomen organs using medical imaging has many
potential applications in clinical workflows. Recently, the state-of-the-art
performance for organ segmentation has been achieved by deep learning models,
i.e., convolutional neural network (CNN). However, it is challenging to train
the conventional CNN-based segmentation models that aware of the shape and
topology of organs. In this work, we tackle this problem by introducing a novel
end-to-end shape learning architecture -- organ point-network. It takes deep
learning features as inputs and generates organ shape representations as points
that located on organ surface. We later present a novel adversarial shape
learning objective function to optimize the point-network to capture shape
information better. We train the point-network together with a CNN-based
segmentation model in a multi-task fashion so that the shared network
parameters can benefit from both shape learning and segmentation tasks. We
demonstrate our method with three challenging abdomen organs including liver,
spleen, and pancreas. The point-network generates surface points with
fine-grained details and it is found critical for improving organ segmentation.
Consequently, the deep segmentation model is improved by the introduced shape
learning as significantly better Dice scores are observed for spleen and
pancreas segmentation.Comment: Accepted to International Workshop on Machine Learning in Medical
Imaging (MLMI2019