In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

We examined optokinetic and optomotor responses of 450 zebrafish mutants, which were isolated previously based on defects in organ formation, tissue patterning, pigmentation, axon guidance, or other visible phenotypes. These strains carry single point mutations in Ͼ400 essential loci. We asked which fraction of the mutants develop blindness or other types of impairments specific to the visual system. Twelve mutants failed to respond in either one or both of our assays. Subsequent histological and electroretinographic analysis revealed unique deficits at various stages of the visual pathway, including lens degeneration (bumper), melanin deficiency (sandy), lack of ganglion cells (lakritz), ipsilateral misrouting of axons (belladonna), optic-nerve disorganization ( grumpy and sleepy), inner nuclear layer or outer plexiform layer malfunction (noir, dropje, and possibly steifftier), and disruption of retinotectal impulse activity (macho and blumenkohl). Surprisingly, mutants with abnormally large or small eyes or severe wiring defects frequently exhibit no discernible behavioral deficits. In addition, we identified 13 blind mutants that display outer-retina dystrophy, making this syndrome the single-most common cause of inherited blindness in zebrafish. Our screen showed that a significant fraction (ϳ5%) of the essential loci also participate in visual functions but did not reveal any systematic genetic linkage to particular morphological traits. The mutations uncovered by our behavioral assays provide distinct entry points for the study of visual pathways and set the stage for a genetic dissection of vertebrate vision.

Two-photon excitation microscopy was used to reconstruct cell divisions in living zebrafish embryonic retinas. Contrary to proposed models for vertebrate asymmetric divisions, no apico-basal cell divisions take place in the zebrafish retina during the generation of postmitotic neurons. However, a surprising shift in the orientation of cell division from central-peripheral to circumferential occurs within the plane of the ventricular surface. In the sonic you (syu) and lakritz (lak) mutants, the shift from central-peripheral to circumferential divisions is absent or delayed, correlating with the delay in neuronal differentiation and neurogenesis in these mutants. The reconstructions here show that mitotic cells always remain in contact with the opposite basal surface by means of a thin basal process that can be inherited asymmetrically.

Abstract. We present a method for automatic segmentation of highgrade gliomas and their subregions from multi-channel MR images. Besides segmenting the gross tumor, we also differentiate between active cells, necrotic core, and edema. Our discriminative approach is based on decision forests using context-aware spatial features, and integrates a generative model of tissue appearance, by using the probabilities obtained by tissue-specific Gaussian mixture models as additional input for the forest. Our method classifies the individual tissue types simultaneously, which has the potential to simplify the classification task. The approach is computationally efficient and of low model complexity. The validation is performed on a labeled database of 40 multi-channel MR images, including DTI. We assess the effects of using DTI, and varying the amount of training data. Our segmentation results are highly accurate, and compare favorably to the state of the art.