Evaluation Oriented Training via Surrogate Metrics for Multiple Sclerosis Segmentation
In current approaches to automatic segmentation of multiple sclerosis (MS) lesions, the segmentation model is not optimized with respect to all relevant evaluation metrics at once, leading to unspecific training. An obstacle is that the computation of relevant metrics is three dimensional (3D) The high computational costs of 3D metrics make their use impractical as learning targets for iterative training. In this paper, we propose an oriented training strategy that employs cheap 2D metrics as surrogates for expensive 3D metrics. We optimize a simple multilayer perceptron (MLP) network as segmentation model. We study fidelity and efficiency of surrogate 2D metrics. We compare oriented training to unspecific training. The results show that oriented training produces a better balance between metrics surpassing unspecific training on average. The segmentation quality obtained with a simple MLP through oriented training is comparable to the state of the art; this includes a recent work using a deep neural network, a more complex model. By optimizing all relevant evaluation metrics at once, oriented training can improve MS lesion segmentation.
MICCAI