18 research outputs found
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
A myelin gene causative of a catatonia-depression syndrome upon aging
Severe mental illnesses have been linked to white matter abnormalities, documented
by postmortem studies. However, cause and effect have remained difficult
to distinguish. CNP (20,30-cyclic nucleotide 30-phosphodiesterase) is among the
oligodendrocyte/myelin-associated genes most robustly reduced on mRNA and
protein level in brains of schizophrenic, bipolar or major depressive patients. This
suggests that CNP reduction might be critical for a more general disease process
and not restricted to a single diagnostic category. We show here that reduced
expression of CNP is the primary cause of a distinct behavioural phenotype, seen
only upon aging as an additional ‘pro-inflammatory hit’. This phenotype is strikingly
similar in Cnp heterozygous mice and patients with mental disease carrying
the AA genotype at CNP SNP rs2070106. The characteristic features in both species
with their partial CNP ‘loss-of-function’ genotype are best described as ‘catatoniadepression’
syndrome. As a consequence of perturbed CNP expression, mice show
secondary low-grade inflammation/neurodegeneration. Analogously, in man, diffusion
tensor imaging points to axonal loss in the frontal corpus callosum. To
conclude, subtle white matter abnormalities inducing neurodegenerative changes
can cause/amplify psychiatric diseases.peerReviewe