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Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image. This process has been shown to have application in many medical image analysis tasks including imputation, registration, and segmentation. To carry out synthesis, the intensities of the input images are typically scaled—i.e., normalized—both in training to learn the transformation and in testing when applying the transformation, but it is not presently known what type of input scaling is optimal. In this paper, we consider seven different intensity normalization algorithms and three different synthesis methods to evaluate the impact of normalization. Our experiments demonstrate that intensity normalization as a preprocessing step improves the synthesis results across all investigated synthesis algorithms. Furthermore, we show evidence that suggests intensity normalization is vital for successful deep learning-based MR image synthesis.

The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients. We present a refinement for finer grained parsing of SDI results in situations where the number of objects is unknown. We explore these ideas with two case studies showing what can be learned from our two presented studies. Our first study explores an inter-rater comparison, showing that smaller lesions cannot be reliably identified. In our second case study, we demonstrate fusing multiple MS lesion segmentation algorithms based on the insights into the algorithms provided by our analysis to generate a segmentation that exhibits improved performance. This work demonstrates the wealth of information that can be learned from refined analysis of medical image segmentations. Segmentation is one of the cornerstones of image processing; it is the process of automatic or semi-automatic detection of boundaries within an image. In a medical imaging context, segmentation is concerned with differentiating tissue classes (i.e., white matter vs. gray matter in the brain), identifying anatomy, or pathology. Motivating examples for the use of segmentation in medical imaging include content based image retrieval 1 , tumor delineation 2 , cell detection 3 and motion tracking 4 , object measurement for size 5 , shape analysis 6 evaluation, and myriad other uses 7-47. The review articles by Pham et al. 48 and Sharma et al. 49 are a useful resource, providing an overview of the different applications of segmentation in medical imaging. A common feature of all this literature is the evaluation of the proposed segmentation algorithm either in comparison to previous work, or more importantly, to some manual/digital gold-standard. In fact, it is impossible to report new segmentation methods without such evaluation; it therefore follows that evaluating medical image segmentation algorithms is important. There have been many methods employed in the evaluation of medical image segmentation algorithms. Voxel-based methods such as intra-class correlation coefficient (ICC) 50,51 , Sørensen-Dice Index 52,53 , and Jaccard coefficient 54 can provide insight about the agreement between a ground truth segmentation and an algorithm

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Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians. However, standard deep neural networks do not provide a reliable measure of uncertainty in those quantitative values. Recent work has shown that using dropout during training and testing can provide estimates of uncertainty. In this work, we investigate using dropout to estimate epistemic and aleatoric uncertainty in a CT-to-MR image translation task. We show that both types of uncertainty are captured, as defined, providing confidence in the output uncertainty estimates.