A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease

Abstract

BACKGROUND AND PURPOSE: Total brain volume and total intracranial volume are important measures for assessing whole-brain atrophy in Alzheimer disease, dementia, and other neurodegenerative diseases. Unlike MR imaging, which has a number of well-validated fully-automated methods, only a handful of methods segment CT images. Available methods either use enhanced CT, do not estimate both volumes, or require formal validation. Reliable computation of total brain volume and total intracranial volume from CT is needed because head CTs are more widely used than head MRIs in the clinical setting. We present an automated head CT segmentation method (CTseg) to estimate total brain volume and total intracranial volume. MATERIALS AND METHODS: CTseg adapts a widely used brain MR imaging segmentation method from the Statistical Parametric Mapping toolbox using a CT-based template for initial registration. CTseg was tested and validated using head CT images from a clinical archive. RESULTS: CTseg showed excellent agreement with 20 manually segmented head CTs. The intraclass correlation was 0.97 (P , .001) for total intracranial volume and 0.94 (P , .001) for total brain volume. When CTseg was applied to a cross-sectional Alzheimer disease dataset (58 with Alzheimer disease patients and 58 matched controls), CTseg detected a loss in percentage total brain volume (as a percentage of total intracranial volume) with age (P , .001) as well as a group difference between patients with Alzheimer disease and controls (P , .01). We observed similar results when total brain volume was modeled with total intracranial volume as a confounding variable. CONCLUSIONS: In current clinical practice, brain atrophy is assessed by inaccurate and subjective "eyeballing" of CT images. Manual segmentation of head CT images is prohibitively arduous and time-consuming. CTseg can potentially help clinicians to automatically measure total brain volume and detect and track atrophy in neurodegenerative diseases. In addition, CTseg can be applied to large clinical archives for a variety of research studies. ABBREVIATIONS: AD 4 Alzheimer disease; BET 4 Brain Extraction Tool; ICC 4 intraclass correlation coefficient; TBV 4 total brain volume; TIV 4 total intracranial volume; TPM 4 tissue probability map

Novel Multi-Linear Quantitative Brain Volume Formula For Manual Radiological Evaluation Of Brain Atrophy

“…The value is not far from our maximum DBF value of 0.88, but CSF and BLOOD are also components of intracranial volume that need technical consideration to delineate. DBF values also are in close alignment with the recently published new automated method of brain volume estimation using CT scan by comparing total brain volume (TBV) from total intracranial volume (TIV) whereas the binarized estimate between automated and manual method had perfect agreement of 0.94 and 0.97 while the probabilistic estimate had lower agreement of 0.74 and 0.71 respectively (40). In the DBF scale, it was found that the value above 0.75 represents the threshold score of the normal brain volume.…”

Brain morphometry in adult: volumetric visualization as a tool in image processing

“…With such tools, decisions are made in real-time or photographs and samples are taken for diagnosis and recommended therapy. Other applications not only refer to medical diagnosis but also to medical planning, development and subsequent evaluation of surgical and radiotherapy 6,[32][33][34] . The 80s marked the emergence of a new type of technology related to medical services, the image archiving and communication system (picture archiving and communication system [PACS]), which represents hardware and software systems, consisting of a digital network, capable of storing, exchanging and view medical images 34 .…”

“…In a later interpretation step, the regions can be identified and labeled with meaningful terms such as "white matter" or "ventricle." Although segmentation is fairly easy for a human expert, it has proven to be extremely difficult for a computer 32,39 .…”

“…Furthermore, it is necessary to repeat the segmentation in a certain number of examinations to assess the inter reviewer and intra reviewer reliability, which is why it is still considered a pending problem to be solved in the quantitative morphometric analysis; being necessary to achieve greater automation of the same. Among the potential benefits of automation are: more reproducible results, improves consistency and reliability, increases the availability of the analysis, reduction of human time required to perform the segmentation allowing to analyze more subjects and allow more comprehensive analysis 32 .…”

“…Some well-validated methods segment only TIV but not TBV. However, TBV is more indicative of pathological conditions in neurodegenerative diseases and TIV is used simply as a variable for normalization purposes 32,39 .…”