A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts

Synaptic Dysbindin-1 Reductions in Schizophrenia Occur in an Isoform-Specific Manner Indicating Their Subsynaptic Location

Background: An increasing number of studies report associations between variation in DTNBP1, a top candidate gene in schizophrenia, and both the clinical symptoms of the disorder and its cognitive deficits. DTNBP1 encodes dysbindin-1, reduced levels of which have been found in synaptic fields of schizophrenia cases. This study determined whether such synaptic reductions are isoform-specific. Methodology/Principal Findings: Using Western blotting of tissue fractions, we first determined the synaptic localization of the three major dysbindin-1 isoforms (A, B, and C). All three were concentrated in synaptosomes of multiple brain areas, including auditory association cortices in the posterior half of the superior temporal gyrus (pSTG) and the hippocampal formation (HF). Tests on the subsynaptic tissue fractions revealed that each isoform is predominantly, if not exclusively, associated with synaptic vesicles (dysbindin-1B) or with postsynaptic densities (dysbindin-1A and -1C). Using Western blotting on pSTG (n = 15) and HF (n = 15) synaptosomal fractions from schizophrenia cases and their matched controls, we discovered that synaptic dysbindin-1 is reduced in an isoform-specific manner in schizophrenia without changes in levels of synaptophysin or PSD-95. In pSTG, about 92% of the schizophrenia cases displayed synaptic dysbindin-1A reductions averaging 48% (p = 0.0007) without alterations in other dysbindin-1 isoforms. In the HF, by contrast, schizophrenia cases displayed normal levels of synaptic dysbindin-1A, but 67% showed synaptic reductions in dysbindin-1B averaging 33% (p = 0.0256), while 80% showed synaptic reductions in dysbindin-1C averaging 35% (p = 0.0171). Conclusions/Significance: Given the distinctive subsynaptic localization of dysbindin-1A, -1B, and -1C across brain regions, the observed pSTG reductions in dysbindin-1A are postsynaptic and may promote dendritic spine loss with consequent disruption of auditory information processing, while the noted HF reductions in dysbindin-1B and -1C are both presynaptic and postsynaptic and could promote deficits in spatial working memory