Robust skull stripping using multiple MR image contrasts insensitive to pathology

Abstract

Automatic skull-stripping or brain extraction of magnetic resonance (MR) images is often a fundamental step in many neuroimage processing pipelines. The accuracy of subsequent image processing relies on the accuracy of the skull-stripping. Although many automated stripping methods have been proposed in the past, it is still an active area of research particularly in the context of brain pathology. Most stripping methods are validated on T₁-w MR images of normal brains, especially because high resolution T₁-w sequences are widely acquired and ground truth manual brain mask segmentations are publicly available for normal brains. However, different MR acquisition protocols can provide complementary information about the brain tissues, which can be exploited for better distinction between brain, cerebrospinal fluid, and unwanted tissues such as skull, dura, marrow, or fat. This is especially true in the presence of pathology, where hemorrhages or other types of lesions can have similar intensities as skull in a T₁-w image. In this paper, we propose a sparse patch based Multi-cONtrast brain STRipping method (MONSTR),² where non-local patch information from one or more atlases, which contain multiple MR sequences and reference delineations of brain masks, are combined to generate a target brain mask. We compared MONSTR with four state-of-the-art, publicly available methods: BEaST, SPECTRE, ROBEX, and OptiBET. We evaluated the performance of these methods on 6 datasets consisting of both healthy subjects and patients with various pathologies. Three datasets (ADNI, MRBrainS, NAMIC) are publicly available, consisting of 44 healthy volunteers and 10 patients with schizophrenia. Other three in-house datasets, comprising 87 subjects in total, consisted of patients with mild to severe traumatic brain injury, brain tumors, and various movement disorders. A combination of T₁-w, T₂-w were used to skull-strip these datasets. We show significant improvement in stripping over the competing methods on both healthy and pathological brains. We also show that our multi-contrast framework is robust and maintains accurate performance across different types of acquisitions and scanners, even when using normal brains as atlases to strip pathological brains, demonstrating that our algorithm is applicable even when reference segmentations of pathological brains are not available to be used as atlases.

Keywords: Atlas; Brain extraction; Non-local; Patches; Segmentation; Skull stripping; Sparsity.

Figure 1

(a) and (b) show axial and coronal orientations of a healthy subject, where brainmasks from 5 different skullstripping methods, BEaST (Eskildsen et al., 2012), SPECTRE (Carass et al., 2011), OptiBET (Lutkenhoff et al., 2014), ROBEX (Iglesias et al., 2011), and our multi-contrast approach called MONSTR, are compared. MONSTR, which use both T₁ and T₂-w images, minimizes inclusion of extracranial tissues. Other methods include parts of skull and marrow. (c) shows a patient with congenital malformation and (d) with severe TBI. Note that lesions can be hypointense (c) or hyperintense (d) on T1-w images. BEaST removes both types of lesions from the mask, and other T1-w image based methods include parts of the skull (yellow arrow). MONSTR retains the lesions within the brain mask while including most of the intracranial tissues. The first two rows used T₁ images to demonstrate cases where segmentations erroneously included tissues outside of the brain (e.g. bone marrow). The bottom rows used T₂ images to better illustrate segmentation errors that did not include the entire intracranial vault (eg. CSF was outside the mask).

Figure 2

An example of T₁-w, T₂-w and registered atlases are shown. 4 atlases are registered via approximate ANTS (see Sec. 2.3) to a subject T₁-w image. The average of the transformed atlas brain masks are overlaid on the subject. The color indicates initial fuzzy brainmask.

Figure 3

The figure shows an independent evaluation scheme of brain masks via CT images. (a)–(b) show T₁-w and T₂-w images of one patient from MOV-32 dataset, where the brain mask from BEaST is overlaid on (c) the CT. A zoomed view of the CT is shown in (d), where two voxels on the brain mask boundary are considered (black boxes). A 3 × 3 × 3 neighborhood is chosen for each boundary voxel. In that neighborhood, “inside” and “outside” voxels (yellow and red boxes, respectively), are considered, as obtained from the binary mask,. The ratio of median CT intensities of “outside voxels” (red boxes) and “inside voxels” (yellow boxes) is computed for every boundary voxel. If the ratio ≫ 1 (e.g., upper voxel #1), then that voxel (#1) is on brain-skull boundary. If the ratio ≈ 1 (e.g., lower voxel #2), then it is completely within brain or within bone. A high ratio is desired for a good stripping mask. See Sec. 2.5 for details.

Figure 4

(a) Dice coefficients between brain masks generated by MONSTR and manual masks are plotted for the ADNI-29 dataset. 6 subjects are chosen as atlases and the remaining 23 subjects are stripped using 1 – 6 atlases. (b) Dice coefficients of 25 subjects are plotted for various search window sizes from s = 1 (3 × 3 × 3) to s = 6 (13 × 13 × 13). Number of atlases used is 4.

Figure 5

Figure shows MPRAGE and T₂ images of two subjects from ADNI-29 dataset along with the stripping masks from 5 algorithms overlaid on T₂. While BEaST and SPECTRE overestimate by including some skull and fat in the mask (yellow arrows), OptiBET and ROBEX underestimate by removing some CSF (green arrows). MONSTR generates a comparatively better mask by considering multiple contrasts.

Figure 6

(a) Dice coefficients and (b) average symmetric surface distances (d_S) between automated and manual brain masks are plotted for 25 subjects from ADNI-29 dataset. MONSTR produces significantly higher Dice (p < 0.001) and lower d_S (p < 0.001) compared to the other 4 methods.

Figure 7

Figure shows MPRAGE and T₂ images of a patient with schizophrenia from NAMIC-20 dataset along with the stripping masks from 5 algorithms overlaid on T₂. Similar to the ADNI-29 dataset, BEaST and SPECTRE include some skull and marrow (yellow arrow), while ROBEX and OptiBET exclude some subarachnoid CSF (green arrow).

Figure 8

(a) Dice coefficients and (b) average symmetric surface distances (d_S) between automated and manual brain masks are plotted for 16 subjects from NAMIC-20 dataset. MONSTR produces significantly higher Dice (p < 0.001) and lower d_S (p < 0.001) compared to the other 4 methods.

Figure 9

The figure shows T₁ and T₂-w images of a patient with severe TBI from the TBI-19. The manual brainmask is overlaid on the T₂. The stripping masks from 5 different stripping methods are compared.

Figure 10

The figure shows comparison of skull stripping performance in the presence of large arachnoid cysts and extraxial fluid collections/hematomas in various locations. Four cases from the Acute dataset (see Sec. 2.1 for details) are presented, where original MPRAGE and T₂-w images are shown. (a) shows a large infarct and an overlying extraaxial fluid collection. Only MONSTR completely segments the intracranial contents. (b) shows two extraaxial collections, a chronic subdural hematoma on the right and a subacute epidural hematoma on the left. (c) shows a large posterior fossa arachnoid cyst/mega cisterna magna. (d) shows a large middle cranial fossa arachnoid cyst. MONSTR virtually completely segments the intracranial contents in (a)–(c), and nearly completerly segments in (d), where the performance of MONSTR is still superior in comparison to the other methods.

Figure 11

(a) Dice coefficients and (b) average symmetric surface distances (d_S) between automated and manual brain masks are plotted for 16 subjects from TBI-19 dataset. MONSTR produces significantly higher Dice (p < 0.001) and lower d_S (p < 0.001) compared to the other 4 methods.

Figure 12

Percent of erroneous boundary voxels (Sec. 2.5) are shown for (a) MOV-32 and (b) TUMOR-36 datasets.

Figure 13

Postcontrast T₁ and T₂ images of a patient from TUMOR-36 are shown, along with brain masks obtained from 5 methods.

Figure 14

(a) Dice coefficients for NAMIC-20 dataset are shown when only T₁ and only T₂ images are used for skull-stripping in the MONSTR framework, as compared to the complete multi-channel T₁ and T₂ images. (b) The effect of resolution is shown on the NAMIC-20 dataset, when the images are downsampled in the inferior-superior direction by a factor of 2 – 5.

Figure 15

Each of (a) ADNI-29, (a) NAMIC-20, and (c) TBI-19 datasets are stripped with atlases chosen from the other two. See Sec. 3.10 for details.