Statistical inference of the inter-sample Dice distribution for discriminative CNN brain lesion segmentation models
This work provides a confidence-based decision rule for clinicians to accept or reject a CNN segmentation model for a specific patient, addressing the challenge of unknown ground truth in clinical practice.
This paper proposes a method to assess the robustness of discriminative CNN brain lesion segmentation models by analyzing the inter-sample Dice distribution on new patients using only MR images. When applied to the ISLES 2015 (SISS) dataset, the model identified 7 predictions as non-robust, leading to a 12% improvement in the average Dice coefficient on the remaining brains.
Discriminative convolutional neural networks (CNNs), for which a voxel-wise conditional Multinoulli distribution is assumed, have performed well in many brain lesion segmentation tasks. For a trained discriminative CNN to be used in clinical practice, the patient's radiological features are inputted into the model, in which case a conditional distribution of segmentations is produced. Capturing the uncertainty of the predictions can be useful in deciding whether to abandon a model, or choose amongst competing models. In practice, however, we never know the ground truth segmentation, and therefore can never know the true model variance. In this work, segmentation sampling on discriminative CNNs is used to assess a trained model's robustness by analyzing the inter-sample Dice distribution on a new patient solely based on their magnetic resonance (MR) images. Furthermore, by demonstrating the inter-sample Dice observations are independent and identically distributed with a finite mean and variance under certain conditions, a rigorous confidence based decision rule is proposed to decide whether to reject or accept a CNN model for a particular patient. Applied to the ISLES 2015 (SISS) dataset, the model identified 7 predictions as non-robust, and the average Dice coefficient calculated on the remaining brains improved by 12 percent.