Reliability of measuring regional callosal atrophy in neurodegenerative diseases

Abstract

The Corpus Callosum (CC) is an important structure connecting the two brain hemispheres. As several neurodegenerative diseases are known to alter its shape, it is an interesting structure to assess as biomarker. Yet, currently, the CC-segmentation is often performed manually and is consequently an error prone and time-demanding procedure. In this paper, we present an accurate and automated method for corpus callosum segmentation based on T1-weighted MRI images. After the initial construction of a CC atlas based on healthy controls, a new image is subjected to a mid-sagittal plane (MSP) detection algorithm and a 3D affine registration in order to initialise the CC within the extracted MSP. Next, an active shape model is run to extract the CC. We calculated the reliability of most popular CC features (area, circularity, corpus callosum index and thickness profile) in healthy controls, Alzheimer's Disease patients and Multiple Sclerosis patients. Importantly, we also provide inter-scanner reliability estimates. We obtained an intra-class correlation coefficient (ICC) of over 0.95 for most features and most datasets. The inter-scanner reliability assessed on the MS patients was remarkably well and ranged from 0.77 to 0.97. In summary, we have constructed an algorithm that reliably detects the CC in 3D T1 images in a fully automated way in healthy controls and different neurodegenerative diseases. Although the CC area and the circularity are the most reliable features (ICC > 0.97); the reliability of the thickness profile (ICC > 0.90; excluding the tip) is sufficient to warrant its inclusion in future clinical studies.

Keywords: Alzheimer's disease; Biomarker; Corpus callosum segmentation; Corpus callosum thickness profile; Multiple sclerosis; Repeatability; Reproducibility.

Fig. 1

Overview of the segmentation pipeline.

Fig. 2

After an affine 3D registration, an average CC is initialised on to the MSP (A – Green line). For every point on the boundary, the optimal translation is calculated along the local normal by matching the intensity profile with intensity profiles observed in the training set at that point. This results in the whimsical shape (Fig. 2.B. Red dots). This shape is projected onto the first N eigenvectors observed in the training data (C. Green line: initial shape, Blue line: result after one iteration), which is used as the starting point of the following cycle. The algorithm continues until convergence (mean movement of red dots < 0.5 mm) or until the maximum number of iterations is reached. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Evolution of the repeatability (ICC) over the CC thickness profile, calculated in 215 healthy control subjects. The colour indicates the repeatability (scale from 0.75 to 1.00). The plotted CC is the average CC obtained in the training set (for illustration purposes only).