icobrain

icobrain is an automated software tool for quantitative analysis of brain structures from MR and NCCT images, intended to streamline neurological image assessment. It employs classical machine learning and deep learning (Convolutional Neural Networks) for segmentation to provide volumetric data and specific reports for conditions such as dementia and Traumatic Brain Injury, supporting both single time-point analysis (MR/NCCT) and longitudinal tracking (MR).

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. It aims to automate the manual process of identifying, labeling, and quantifying these brain structures. The software utilizes two distinct image processing pipelines: 'icobrain cross' for analyzing images (MR or NCCT) from a single time point, and 'icobrain long' for assessing changes in brain volumes between two MR images acquired at different timepoints on the same scanner with identical protocols; the results from these two pipelines are not comparable. The software processes input DICOM images by converting them to NIFTI format, then applies image segmentation algorithms, including classical machine learning and deep learning (specifically Convolutional Neural Networks for CT), to calculate measurements of brain structures and abnormalities. A key update is its enhanced CT processing capability, which generates specific reports for potential dementia patients (e.g., whole brain and lateral ventricle volumes) and traumatic brain injury (TBI) patients (e.g., midline shift, basal cistern volumes, hyperdensity volumes). Performance testing on 544 subject datasets, including TBI and dementia cohorts, demonstrated the CT pipeline's accuracy and reproducibility, achieving an average Pearson correlation coefficient of 0.95 and an intraclass correlation coefficient of 0.94 against ground truth or outputs from prior software versions.

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• Q1. What specific Convolutional Neural Network (CNN) architectures are employed for the CT image segmentation, and how were they validated across diverse patient populations and CT scanner variations?; How does icobrain handle variations in image quality or common artifacts in clinical MR and NCCT scans, and what is its reported failure rate or the typical need for manual review/correction?; Beyond overall correlation coefficients, what are the quantitative performance metrics (e.g., Dice coefficient, absolute volume difference) of the 'icobrain cross' CT pipeline for specific structures like basal cisterns or hyperdensities when compared to expert manual segmentation?; For the 'icobrain long' MR pipeline, what is the minimum detectable volumetric change for key brain structures, and how does this sensitivity compare to established rates of pathological progression in relevant neurological diseases?; Are there plans to integrate findings from the CT-derived dementia and TBI reports, or to correlate them with MR-derived metrics, for patients presenting with complex or comorbid neurological conditions?