RADIUS – THE AI BACKBONE OF CEREBRIU SOLUTIONS

Radius is the AI and deep learning based image quantification backbone of all Cerebriu clinical solutions.
It is based on 50+ man years of research and development – explore some core Radius features below.

2013

Atrophy

Whole brain, ventricle, and hippocampal atrophy measurements are important biomarkers for especially Alzheimer’s disease. Radius offers single MRI, T1 atrophy quantification through brain segmentation and reference populations (see Brain segmentation in the 2018 timeline entry).

Radius, however, also offers atrophy rate quantification using advanced registration, simultaneous bias correction, and accurate volume difference calculations. The is important for clinical monitoring of patient disease progression but also plays a significant role in efficacy quantification for clinical drug trials.

Keywords:
Atrophy rate
Cube propagation
Bias correction

2014

Hippocampal Texture – early detection of Alzheimer’s disease using MR hippocampal texture.

Changes in hippocampal texture relating to Alzheimer’s disease can be imaged and quantified earlier in the disease trajectory than atrophy of subcortical structures using Radius.

Radius hippocampal texture quantification was a cornerstone of our winning 2014 CADmentia grand challenge entry.

The oral presentation of the methodology at the subsequent European Conference of Radiology 2015 won conference best neuro presentation.

Keywords:
Hippocampal texture
CADDementia

2018a

2018a White matter lesions

Segmentation and quantification of white matter lesions from brain MRI is an important driver to understand vascular pathology in many neurological disorders. White matter lesions or white matter hypo-/hyperintensities are typically quantified from T1 or FLAIR sequences respectively.

Radius supports automatic quantification of white matter lesions from both T1 and FLAIR. Additionally, a novel Cerebriu approach improves T1-based quantification by simultaneous and coupled imputation or synthesis of potentially unavailable FLAIR sequences.

Keywords:
Quantification of white matter lesions

2018b

Brain segmentation

State-of-the-art brain segmentation using deep learning. The underlying deep-learning based versatile segmentation engine resulted in a top-ranked entry for the recent Medical Segmentation Decathlon Grand Challenge.

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