In multi-atlas segmentation, multiple atlases are registered to the target ( Avants et al., 2008 Ourselin et al., 2001) and the resulting voxelwise label conflicts are resolved using label fusion ( Artaechevarria et al., 2009 Asman and Landman, 2012c Coupé et al., 2011 Sabuncu et al., 2010 Wang et al., 2012 Warfield et al., 2004). Over the past decade, multi-atlas segmentation has come to represent the de facto standard segmentation framework for its ability to rapidly and accurately generalize structural information from labeled examples (i.e., atlases) ( Heckemann et al., 2006 Rohlfing et al., 2004b).
![volumetrix example mri volumetrix example mri](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41598-021-93227-3/MediaObjects/41598_2021_93227_Fig1_HTML.jpg)
The goal of this manuscript is to provide an efficient and accurate segmentation framework specifically focused on segmenting the spinal cord’s internal structure and enabling future clinically relevant inference about the anatomy and its associated conditions. Given the challenges associated with spinal anatomy, imaging data, and subsequent processing, developing a robust system to consistently and accurately overcome these challenges is essential. Moreover, MRI of the cervical spinal cord is hindered by numerous technical challenges ( Hinks and Quencer, 1988 Karpova et al., 2013 Mikulis et al., 1994) – including (1) the small dimensions (1-2 cm in diameter) with subsequent signal to noise limitations, (2) similarity between WM and GM T1 and T2 values resulting in poor intra-cord contrast ( Smith et al., 2008), (3) involuntary/physiological patient motion, and (4) imaging in homogeneities and artifacts.
![volumetrix example mri volumetrix example mri](https://www.researchgate.net/publication/326832721/figure/fig1/AS:960094925762584@1605915876614/Example-of-volumetric-assessment-of-a-chiasmatic-hypothalamic-lesion-using-MRI.png)
In fact, high-resolution MRI that can provide contrast among spinal cord internal structures has only recently become feasible in clinically acceptable scan times ( Farrell et al., 2008 Ozturk et al., 2013 Smith et al., 2010 Yiannakas et al., 2012 Zackowski et al., 2009). While automated methods have been used to segment the spinal cord from the surrounding cerebrospinal fluid (CSF)( Carballido-Gamio et al., 2004 Chen et al., 2011 Huang et al., 2009 Ma et al., 2010 McIntosh and Hamarneh, 2006) and semi-automated methods have been used for more detailed parcellation of the individual spinal columns( Horsfield et al., 2010 Kaminsky et al., 2004), automated delineation of internal spinal cord structures (i.e., GM/WM) has not been reported for any imaging modalities. Differentiating and localizing pathology/degeneration of the gray matter (GM) and white matter (WM) plays a critical role in assessing the magnitude of tissue damage, therapeutic impacts and determining prognosis of these conditions ( Gilmore et al., 2006 Jarius and Wildemann, 2010). The spinal cord is an essential and vulnerable component of the central nervous system which can be significantly affected by numerous neurological conditions – e.g., amyotrophic lateral sclerosis, multiple sclerosis, and neuromyelitis optica ( Bede et al., 2013 Bede et al., 2012 Dietz and Curt, 2006 Wingerchuk et al., 2007 Yiannakas et al., 2012). In a cross-validation experiment using 67 MR volumes of the cervical spinal cord, we demonstrate sub-millimetric accuracy, significant quantitative and qualitative improvement over comparable multi-atlas frameworks, and provide insight into the sensitivity of the associated model parameters.
#VOLUMETRIX EXAMPLE MRI FREE#
Moreover, the proposed framework provides a natural mechanism for performing atlas selection and initializing the free model parameters in an informed manner.
#VOLUMETRIX EXAMPLE MRI REGISTRATION#
Specifically, we provide a method for (1) pre-aligning the slice-based atlases into a groupwise-consistent space, (2) constructing a model of spinal cord variability, (3) projecting the target slice into the low-dimensional space using a model-specific registration cost function, and (4) estimating robust segmentations using geodesically appropriate atlas information. Herein, we present a novel slice-based groupwise registration framework for robustly segmenting cervical spinal cord MRI. Thus, to date, no automated algorithms have been presented for the spinal cord’s internal structure. Additionally, due to the inter-subject variability exhibited on cervical MRI, typical deformable volumetric registrations perform poorly, limiting the applicability of a typical multi-atlas segmentation framework. Yet, low contrast-to-noise ratio, artifacts, and imaging distortions have limited the applicability of tissue segmentation techniques pioneered elsewhere in the central nervous system. Fortunately, new magnetic resonance imaging (MRI) sequences enable clinical study of the in vivo spinal cord’s internal structure. white matter) is critical for assessment of therapeutic impacts and determining prognosis of relevant conditions.
![volumetrix example mri volumetrix example mri](https://www.researchgate.net/profile/Hadi-Hasanzadeh/publication/257649945/figure/fig1/AS:392592865939459@1470612841917/Segmentation-method-example-for-caudate-nucleus-on-a-T-2-weighted-MRI-scan-A-C-in.png)
![volumetrix example mri volumetrix example mri](https://www.asawicki.info/files/volume_renderer_05_lambert_lighting.png)
Differentiating and localizing the spinal cord internal structure (i.e., gray matter vs. The spinal cord is an essential and vulnerable component of the central nervous system.