In this paper we present a method to segment four brainstem structures (midbrain pons medulla oblongata and superior cerebellar peduncle) from 3D brain MRI scans. Using cross validation we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy (mean error under 1 mm) and robustness (no failures in 383 scans including 168 AD cases). We also indirectly evaluate the algorithm with a experiment in which we study the atrophy of the brainstem in aging. The results show that when used simultaneously the volumes of Rabbit polyclonal to ACVR2B. the midbrain pons and medulla are significantly more predictive of age than the volume of the entire brainstem estimated as their sum. The results SGI-1776 (free base) also demonstrate that that the method can detect atrophy patterns in the brainstem structures that have been previously described in the literature. Finally we demonstrate that the proposed algorithm is able to detect differential effects of AD on SGI-1776 (free base) the brainstem structures. The method will be implemented as part of the popular neuroimaging package FreeSurfer. Graphical abstract 1 Introduction The human brainstem is a complex brain structure consisting of long axons and scattered nuclei. At a high level the brainstem is divided in three structures; from superior to inferior: midbrain pons and medulla oblongata. These structures support different functions: while SGI-1776 (free base) the midbrain is associated with vision hearing sleep and motor control the pons mostly consists of white matter tracts that connect the cerebrum with the medulla. The pons is also connected with the cerebellum through nerve tracts knows as the cerebellar peduncles and contains nuclei associated with functions such as respiration and facial expression. The medulla oblongata connects the rest of the brain to the spinal chord and regulates cardiac and respiratory functions as well as reflexes such as swallowing. Automated segmentation of the brainstem structures can potentially improve our understanding of the role that they play in different functions and how they are affected by neurodegenerative pathologies by circumscribing neuroimaging analyses (e.g. volumetry functional MRI tractography) to these specific regions. The brainstem is especially relevant to diseases with pure underlying tau pathology such as progressive supranuclear palsy and corticobasal degeneration SGI-1776 (free base) also called primary tauopathies. In progressive supranuclear palsy brain atrophy occurs in the midbrain pons and superior cerebellar peduncle due to neuronal loss associated with accumulation of insoluble deposits of abnormal tau protein [1]. New therapies designed to prevent or decrease tau accumulation are rapidly entering human clinical trials and longitudinal brainstem atrophy measurements with MRI – in which automated methods yield reproducible results and allow for much larger sample sizes – have been demonstrated to be useful outcome measures in these studies [2]. Other neurodegenerative diseases in which the brainstem structures are SGI-1776 (free base) also differentially affected include Parkinson’s [3] and Alzheimer’s [4]. In addition to studies of neurodegenerative diseases automated segmentation algorithms for the brainstem structures would also find application in other areas. For instance the pedunculopontine nucleus is a target for the implantation of deep brain stimulators in Parkinson’s disease [5]. The pons is often used as a reference region in positron emission tomography (PET) data since there is no effect of interest in it [6]. Neuroimaging studies of pain [7] [8] have also relied on segmenting brainstem SGI-1776 (free base) structures. Despite all its possible applications the segmentation of the brainstem structures remains largely unexplored in the medical image analysis literature and none of the widely-distributed neuroimaging analysis package performs it so far. Instead most works have aimed at segmenting the brainstem as a whole. [9] used a single labeled template that was deformed towards the novel scan to produce the automated segmentation. [10] proposed a semi-automatic algorithm in which fuzzy connectedness and morphological operations are used to generate a preliminary segmentation which is subsequently refined with active contours. The same authors [11] later proposed a similar though fully automated method in which AdaBoost [12] was used to generate the initial coarse region.