Icobrain

icobrain is medical image processing software that automates the labeling, visualization, and volumetric quantification of segmentable brain structures from MR and NCCT images using cross-sectional and longitudinal analysis pipelines. By employing advanced segmentation algorithms and statistical normalization against healthy population data, it provides quantitative reports on brain volumes and abnormalities to aid in the assessment of various neurological conditions.

icobrain is medical image processing software designed for the automatic labeling, visualization, and volumetric quantification of segmentable brain structures from Magnetic Resonance (MR) or Non-Contrast Computed Tomography (NCCT) images. Its primary purpose is to automate the manual process of identifying, labeling, and quantifying the volume of these brain structures. The software operates using two distinct image processing pipelines: 'icobrain cross' for analyzing images acquired at a single time point (both MR and NCCT), and 'icobrain long' for quantifying changes in volumes between two MR images acquired on the same scanner with the same protocol at different timepoints. Input images in DICOM format are converted to NIFTI format, after which image processing algorithms perform segmentation and calculate measurements of brain structures and any identified abnormalities. The software generates electronic reports detailing various quantitative outputs. For MR images, these include unnormalized volumes and volume changes (total, new, enlarging, shrinking) of FLAIR white matter hyperintensities across different brain regions, T1 white matter hyperintensities and hypointensities, as well as normalized volumes and volume changes for the whole brain, gray matter, frontal, temporal, and parietal lobes, and hippocampi (including an asymmetry index). For NCCT images, outputs are tailored for conditions like potential dementia (normalized whole brain and lateral ventricle volumes) or potential Traumatic Brain Injury (TBI), including unnormalized midline shift, NCCT hyperdensity volumes, and normalized volumes of lateral and fourth ventricles and basal cisterns. Volume normalization for head size is performed by comparing to a healthy population using a statistical model. Performance testing on 463 MR and 618 CT datasets, covering healthy individuals and patients with conditions such as Alzheimer’s disease, multiple sclerosis, TBI, and depression, demonstrated accuracy and reproducibility with average Pearson correlation coefficients of 0.91 for MR and 0.94 for CT when compared to validation data.

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• Q1. What is the sensitivity and specificity of icobrain in detecting subtle white matter hyperintensity changes or early hippocampal atrophy compared to expert neuroradiologist assessment in clinically relevant patient cohorts?; How does the performance of icobrain's segmentation algorithms vary when processing images acquired from different MR scanner vendors, field strengths, or varying NCCT acquisition protocols and image quality?; Can the longitudinal analysis ('icobrain long') reliably differentiate true pathological change from normal physiological fluctuations or test-retest variability in patients with stable or slowly progressing neurological conditions?; What is the statistical methodology and demographic composition (age, sex, ethnicity, health status) of the 'healthy population' dataset used for normalization, and how does this influence the interpretation of volumetric results for individual patients from diverse backgrounds?; Beyond volumetric data, what is the potential for future iterations of icobrain to incorporate or output other quantitative imaging features, such as lesion distribution patterns, shape descriptors, or texture analysis, for a more comprehensive neurological assessment?