29 research outputs found
Cardiac injuries in blunt chest trauma
Blunt chest traumas are a clinical challenge, both for diagnosis and treatment. The use of cardiovascular magnetic resonance can play a major role in this setting. We present two cases: a 12-year-old boy and 45-year-old man. Late gadolinium enhancement imaging enabled visualization of myocardial damage resulting from the trauma
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
Standardised computed tomographic assessment of left atrial morphology and tissue thickness in humans
Aims: Left atrial (LA) remodelling is a common feature of many cardiovascular pathologies and is a sensitive marker of adverse cardiovascular outcomes. The aim of this study was to establish normal ranges
for LA parameters derived from coronary computed tomographic angiography (CCTA) imaging using a
standardised image processing pipeline to establish normal ranges in a previously described cohort.
Methods: CCTA imaging from 193 subjects recruited to the Budapest GLOBAL twin study was analysed.
Indexed LA cavity volume (LACVi), LA surface area (LASAi), wall thickness and LA tissue volume (LATVi)
were calculated. Wall thickness maps were combined into an atlas. Indexed LA parameters were compared with clinical variables to identify early markers of pathological remodelling.
Results: LACVi is similar between sexes (31 ml/m2 v 30 ml/m2
) and increased in hypertension (33 ml/m2 v
29 ml/m2
, p = 0.009). LASAi is greater in females than males (47.8 ml/m2 v 45.8 ml/m2 male, p = 0.031).
Median LAWT was 1.45 mm. LAWT was lowest at the inferior portion of the posterior LA wall (1.14 mm)
and greatest in the septum (median = 2.0 mm) (p < 0.001). Conditions known to predispose to the development of AF were not associated with differences in tissue thickness.
Conclusions: The reported LACVi, LASAi, LATVi and tissue thickness derived from CCTA may serve as reference values for this age group and clinical characteristics for future studies. Increased LASAi in females
in the absence of differences in LACVi or LATVi may indicate differential LA shape changes between the
sexes. AF predisposing conditions, other than sex, were not associated with detectable changes in LAWT.
Clinical trial registration: http://www.ClinicalTrials.gov/NCT01738828
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