LOGISMOS-B: layered optimal graph image segmentation of multiple objects and surfaces for the brain

Abstract

Automated reconstruction of the cortical surface is one of the most challenging problems in the analysis of human brain magnetic resonance imaging (MRI). A desirable segmentation must be both spatially and topologically accurate, as well as robust and computationally efficient. We propose a novel algorithm, LOGISMOS-B, based on probabilistic tissue classification, generalized gradient vector flows and the LOGISMOS graph segmentation framework. Quantitative results on MRI datasets from both healthy subjects and multiple sclerosis patients using a total of 16,800 manually placed landmarks illustrate the excellent performance of our algorithm with respect to spatial accuracy. Remarkably, the average signed error was only 0.084 mm for the white matter and 0.008 mm for the gray matter, even in the presence of multiple sclerosis lesions. Statistical comparison shows that LOGISMOS-B produces a significantly more accurate cortical reconstruction than FreeSurfer, the current state-of-the-art approach (p << 0.001). Furthermore, LOGISMOS-B enjoys a run time that is less than a third of that of FreeSurfer, which is both substantial, considering the latter takes 10 h/subject on average, and a statistically significant speedup.

Fig. 1

Pipeline overview. The pre-processing creates an initial WM surface. This surface, as well as the GGVF method and the regional parcellation are combined together to construct a graph. LOGISMOS-based segmentation generates the final WM and GM surfaces. Finally, the brainstem and cerebellum are removed in post-processing.

Fig. 2

Graph structure. (A) Whole WM surface (white) and the graph nodes (blue spheres). Black inset zoomed in (B), and further zoomed in the rest of panels. (C) Intra-column arcs (red) and graph nodes (blue). (D) Inter-column arcs (green) and graph nodes (blue).

Fig. 3

Regional parcellation of the cortex for the atlas. Colors denote different ROIs. Left: Lateral view. Middle: Medial view. Right: Top view.

Fig. 4

Regional parcellation of the cortex for a typical subject. Colors denote different ROIs. Left: Lateral view. Middle: Medial view. Right: Top view. Note that, because the parcellation is computed using the pre-segmentation surface, these are WM surfaces (unlike the atlas visualization in Fig. 3). Furthermore, note that the pre-segmentation surfaces contain brainstem and cerebellum, which are later removed in post-processing.

Fig. 5

Outline of the reconstructed LOGISMOS-B surfaces on a series of horizontal slices for a healthy subject. Red: GM surface, yellow: WM surface. Manually picked landmark clusters are shown in purple (GM) and blue (WM). CALC: Calcarine sulcus, CING: cingulate, CS: central sulcus, PO: parieto-occipital sulcus, SF: superior-frontal sulcus, ST: superior-temporal sulcus, SYL: Sylvian fissure.

Fig. 6

Outline of the reconstructed FreeSurfer surfaces on a series of horizontal slices for a healthy subject. Red: GM surface, yellow: WM surface. Manually picked landmark clusters are shown in purple (GM) and blue (WM). CALC: Calcarine sulcus, CING: cingulate, CS: central sulcus, PO: parieto-occipital sulcus, SF: superior-frontal sulcus, ST: superior-temporal sulcus, SYL: Sylvian fissure.

Fig. 7

Outline of the reconstructed LOGISMOS-B surfaces on a series of horizontal slices for an MS patient. Red: GM surface, yellow: WM surface. Manually picked landmark clusters are shown in purple (GM) and blue (WM). CALC: Calcarine sulcus, CING: cingulate, CS: central sulcus, PO: parieto-occipital sulcus, SF: superior-frontal sulcus, ST: superior-temporal sulcus, SYL: Sylvian fissure.

Fig. 8

Outline of the reconstructed FreeSurfer surfaces on a series of horizontal slices for an MS patient. Red: GM surface, yellow: WM surface. Manually picked landmark clusters are shown in purple (GM) and blue (WM). CALC: Calcarine sulcus, CING: cingulate, CS: central sulcus, PO: parieto-occipital sulcus, SF: superior-frontal sulcus, ST: superior-temporal sulcus, SYL: Sylvian fissure.

Fig. 9

Reconstructed white matter surfaces for 10 subjects with LOGISMOS-B, top view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 10

Reconstructed white matter surfaces for 10 subjects with FreeSurfer, top view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 11

Reconstructed white matter surfaces for 10 subjects with LOGISMOS-B, side view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 12

Reconstructed white matter surfaces for 10 subjects with FreeSurfer, side view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 13

Reconstructed white matter surfaces for 10 subjects with LOGISMOS-B, medial view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 14

Reconstructed white matter surfaces for 10 subjects with FreeSurfer, medial view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 15

Reconstructed gray matter surfaces for 10 subjects with LOGISMOS-B, top view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 16

Reconstructed gray matter surfaces for 10 subjects with FreeSurfer, top view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 17

Reconstructed gray matter surfaces for 10 subjects with LOGISMOS-B, side view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 18

Reconstructed gray matter surfaces for 10 subjects with FreeSurfer, side view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 19

Reconstructed gray matter surfaces for 10 subjects with LOGISMOS-B, medial view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 20

Reconstructed gray matter surfaces for 10 subjects with FreeSurfer, medial view. Top row: Healthy controls. Bottom row: MS patients.

Fig. 21

Construction of graph columns. (A)–(C) Whole brain; (D)–(F) zoomed in view; (G)–(I) zoomed in view with cross-sectional clipping for illustrating the interior of the surface. Outside of the WM surface is shown in white, the inside in green, the graph columns in red.