Adding Assessa on to your current diagnosis pathway has a number of benefits:

  • Helps healthcare professionals make fast and accurate diagnosis of dementia and other neurological diseases
  • Ensures support is provided quickly to the patient and their carer
  • Enables patients to access the right treatments and support they need and extend their independent life
  • Supports an increasingly personalised approach to medicine and treatment


Assessa has many features that ensure accurate and usable information is reported to clinicians.

Accuracy: Assessa is based upon a large reference database of images that drive the segmentation of the brain regions. The database is derived from high quality manual segmentations and the results have been shown to be consistent with those produced by trained experts performing manual segmentations.

Compatibility: Assessa can be used with any 3D T1-weighted MRI scan from 1.5T and 3T scanners. We provide some basic guidelines to ensure that you receive reliable results.

Reference Data: All results are compared to a large database of age matched normal controls to give a clear indication of the likelihood of any patient having atrophy that is likely to be due to dementia. An example of a typical graph is given below, the graph also plots the distribution for Alzheimer's Disease patients (outline blue plot) as well as normal controls (full blue plot).

Plot showing patient result relative to a normal database

Regions: Absolute volumes for the hippocampus, amygdala and temporal horn are generated by Assessa. Volumes are also corrected for head size and the asymmetry of each region is given.

Regulatory: Assessa is a medical device (Class 2a) that is CE marked for use in the EU and other areas recognising the CE mark

Robustness: Assessa has been applied to thousands of datasets. The algorithms automatically detect, and where possible, correct for poor image quality, for example due to image inhomogeneities.

LEAP- The algorithm that drives Assessa

Assessa is based on the LEAP (Learning Embeddings for Atlas Propagation) algorithm1.

In this method, the brain of the subject being processed is compared to a reference database containing multiple images with pre-segmented brain regions. LEAP uses patented technology to select a subset of the reference images that are most similar to the subject image, specifically taking into account the degenerative changes observed in the demented brain. Aligning all selected reference images to the subject image allows the algorithm to obtain an estimate of the hippocampus localisation in the subject image.

LEAP has been shown to be able to predict time to dementia in subjects with MCI with an accuracy better than that of radiological scoring of Medial Temporal Atrophy2-4 and to be able to add complementary information to an analysis of cerebrospinal fluid, CSF5. LEAP can be applied to multiple brain structures including the hippocampus, amygdala, parahippocampal gyrus and lateral ventricles6, and a combined analysis has been shown to be able to increase the predictive power over that of hippocampal volume alone7.

The test:re-test performance of the LEAP algorithm has been characterized both on repeat scans on a single scanner, and moving the subject between 1.5T and 3T scanners, showing the robustness of the method towards the typical variability in acquisition protocols found between different clinical centers8.

LEAP was one of the algorithms included in the EMA qualification of Low Hippocampal Volume as a biomarker to enrich clinical trials of AD in the pre-dementia phase4. Recent work shows the impact of applying hippocampal enrichment on trial size and cost9.


1.Wolz et al, LEAP: Learning embeddings for atlas propagation, Neuroimage. 2010 January 15;49(2):1316-25.
2. van Rossum et al, Injury markers predict time to dementia in subjects with MCI and amyloid pathology, Neurology. 2012 Oct 23;79(17):1809-16.
3. Clerx et al, Measurements of medial temporal lobe atrophy for prediction of Alzheimer's disease in subjects with mild cognitive impairment, Neurobiol Aging. 2013 Aug;34(8):2003-13.
4. Hill et al, CAMD/EMA Biomarker Qualification of Hippocampal Volume for Enrichment of Clinical Trials in Predementia Stages of Alzheimer’s Disease, Alz and Dem, 2013.
5. Vos et al, Prediction of Alzheimer disease in subjects with amnestic and nonamnestic MCI, Neurology. 2013 Mar 19;80(12):1124-32.
6. Wolz et al, Segmentation of the ADNI database into 83 anatomical regions using LEAP, MICCAI 2011.
7. Wolz et al, Prediction of cognitive and functional decline in patients with mild cognitive impairment by multiple brain volumes automatically extracted from structural MRI, CTAD 2013.
8. Wolz et al, Robustness of automated hippocampal volumetry across MR field strengths and repeat scans, Alz & Dem, 2013.
9. Yu et al,Operationalizing hippocampal volume as an enrichment biomarker for amnestic mild cognitive impairment trials: effect of algorithm, test-retest variability, and cut point on trial cost, duration, and sample size, Neurobiol Aging. 2013.