Semiautomatic segmentation of brain subcortical structures from high-field MRI

Abstract

Volumetric segmentation of subcortical structures, such as the basal ganglia and thalamus, is necessary for noninvasive diagnosis and neurosurgery planning. This is a challenging problem due in part to limited boundary information between structures, similar intensity profiles across the different structures, and low contrast data. This paper presents a semiautomatic segmentation system exploiting the superior image quality of ultrahigh field (7 T) MRI. The proposed approach utilizes the complementary edge information in the multiple structural MRI modalities. It combines optimally selected two modalities from susceptibility-weighted, T2-weighted, and diffusion MRI, and introduces a tailored new edge indicator function. In addition to this, we employ prior shape and configuration knowledge of the subcortical structures in order to guide the evolution of geometric active surfaces. Neighboring structures are segmented iteratively, constraining oversegmentation at their borders with a nonoverlapping penalty. Several experiments with data acquired on a 7 T MRI scanner demonstrate the feasibility and power of the approach for the segmentation of basal ganglia components critical for neurosurgery applications such as deep brain stimulation surgery.

Fig. 1

Schematic overview of the proposed segmentation

Fig. 2

Sigmoid function with β_low = −10, and (a) α = 1, (b) α = −1

Fig. 3

Interpretation g_new on the clear edge and unclear edge in a 1D (in case of α<0). (a) g′ with β_High on T₂W in (14), g′_{High (T2W)}. (b) g′ with βLow on SWI in (14), g′_{Low (SWI)}. (c) Dirac measure for the level set representation of a mean shape, ${\delta }_0\left({\mathrm{u}}_0^{\ast }\right)$. (d) a new edge map, g_new.

Fig. 4

A new edge map generated by combining axial T₂W image with SWI. (a) Axial T₂W image. (b) Axial SWI. (c) ROI of T₂W image. (d) ROI of SWI. (e) Laplacian of smoothed T₂W image. (f) Laplacian of smoothed SWI. (g) g′_{Low (T2W)} with α= 0.5, β_Low= 8. (h) g′_{High (SWI)} with α= 0.5, β_High= 13. (i) ${\delta }_{\epsilon }\left({\mathrm{u}}_0^{\ast }\right)$ with ε= 1. (j) g_new. Note that regions around left GPe and GPi (the red circle) in (j) are improved.

Fig. 5

Image gradient magnitude of the SWI and T₂W images and their corresponding g in (2). (a) Gradient magnitude of smoothed T₂W. (b) Gradient magnitude of smoothed SWI. (c) gT₂W (inverse of (a)). (d) g_SWI (inverse of (b)).

Fig. 6

Iterative segmentation flow for SN and STN within modified GAS framework

Fig. 7

Segmentation results of SN and STN at each iteration. Top shows contours in both axial and coronal slices, and bottom represents the corresponding 3D structures. (a) First iteration: The green and red represent the first segmented SN without the constraint and the first segmented STN with the first SN, respectively. (b) Second iteration: The green and red represent the second segmented SN with the first STN and the first segmented STN with the first SN, respectively. (c) Third iteration: The green and red represent the second segmented SN with the first STN and the second segmented STN with the second SN, respectively. The blue represents manually segmented SN and STN.

Fig. 8

Schematic workflow for the semi-automatic 3D segmentation of basal ganglia components and thalamus

Fig. 9

DC values of segmentations from GAS and our proposed model (without and with non-overlapping constraints), based on three combinations of two single-modal images for each structure. (a) GPe. (b) GPi. (c) SN. (d) STN. (e) CN. (f) Tha. (g) Pu. The left and right columns represent left and right structures, respectively.

Fig. 10

Comparison of segmentation results for GPe and GPi on dataset 3. The light green and brown represent GPe and GPi, respectively. The blue contours represent manual segmentations. Top and bottom in each figure represent contours and volumetric segmentations, respectively. Figures (a), (b), and (c) show segmentation results of GAS, GAS with shape prior using g, GAS with shape prior using g’ on axial T₂W image (left column) and axial SWI (right column), respectively. Figures (d), (e), and (f) are segmentation results of GAS, the proposed approach without non-overlapping constraints, and the proposed approach, respectively, with surface distance maps (right column, top: GPe, bottom: GPi) on axial T₂W image combined with axial SWI.

Fig. 11

Comparison of segmentation results for SN and STN on dataset 3. The red and yellow represent SN and STN, respectively. The blue contours represent manual segmentations. Top and bottom in each figure represent contours and volumetric segmentations, respectively. Figures (a), (b), and (c) show segmentation results of GAS, GAS with shape prior using g, GAS with shape prior using g’ on coronal T₂W image (left column) and coronal SWI (right column), respectively. Figures (d), (e), and (f) are segmentation results of GAS, the proposed approach without non-overlapping constraints, and the proposed approach, respectively, with surface distance maps (right column, top: SN, bottom: STN) on coronal T₂W image combined with coronal SWI.

Fig. 12

Comparison of segmentation results from the single-modality based approaches for CN, Tha, and Pu on dataset 3. The violet, dark green, and cyan represent CN, Tha, and Pu, respectively. The blue contours represent manual segmentations. Top and bottom in each figure represent contours and volumetric segmentations, respectively. Figures (a), (c), and (e) show segmentation results in the one view of CN and Tha from GAS, GAS with shape prior using g, and GAS with shape prior using g’ on FA image (left column) and SWI (right column), respectively. Figures (b), (d), and (f) are segmentation results in another view of CN, Tha, and Pu from GAS, GAS with shape prior using g, GAS with shape prior using g’ on FA image (left column) and SWI (right column), respectively.

Fig. 13

Comparison of segmentation results from the multi-modality based approaches for CN, Tha, and Pu on dataset 3. The violet, dark green, and cyan represent CN, Tha, and Pu, respectively. The blue contours represent manual segmentations. Top and bottom in each figure represent contours and volumetric segmentations, respectively. Figures (a), (b), and (c) show segmentation results in the one view (first column) of CN and Tha and another view (second column) of CN, Tha, and Pu from GAS, the proposed approach without non-overlapping constraints, and the proposed approach, respectively, with surface distance maps (third column (top view) and fourth column (bottom view), first row: CN, second row: Pu and Tha) on the FA image combined with axial SWI. GPe (light green) segmented on T₂W combined with SWI (i.e., segmented GPe in Fig. 10 (f)) is incorporated as contours in (b) and (c), respectively, to see overlaps between Pu and GPe. Note that overlaps between Pu and GPe in (c) are considerably reduced (see top right of (b) and (c)).

Fig. 14

Manual segmentations for each structure on dataset 3. Top left shows GPe (light green) and GPi (brown) on the axial SWI. Top right represents SN (red) and STN (yellow) on the coronal SWI. Bottom left shows Pu (cyan) and GPe (light green) on the FA image. Bottom right represents CN (violet) and Tha (dark green) on the FA image..

Fig. 15

Average DC values and standard errors of segmented results for each approach on data set from 1 to 5. Figures (a) and (b) represent DC values for left and right structures, respectively.

Fig. 16

Comparison of segmentation results from the multi-modality based approaches for GPe and GPi on dataset 6. The light green and brown represent GPe and GPi, respectively. Top and bottom in each figure represent contours and volumetric segmentations, respectively. Figures (a), (b), and (c) show segmentation results of GAS, the proposed approach without non-overlapping constraints, and the proposed approach, respectively, on the axial T₂W image combined with axial SWI.

Fig. 17

Comparison of segmentation results from the multi-modality based approaches for SN and STN on dataset 6. The red and yellow represent SN and STN, respectively. Top and bottom in each figure represent contours and volumetric segmentations, respectively. Figures (a), (b), and (c) show segmentation results of GAS, the proposed approach without non-overlapping constraints, and the proposed approach, respectively, on the coronal T₂W image combined with coronal SWI.

Fig. 18

Comparison of segmentation results from the multi-modality based approaches for CN, Tha and Pu on dataset 6. The violet, dark green, and cyan represent CN, Tha, and Pu, respectively. Top and bottom in each figure represent contours and volumetric segmentations, respectively. Figures (a), (b), and (c) show segmentation results in the one view (left column) and another view (right column) of CN, Tha, and Pu from GAS, the proposed approach without non-overlapping constraints, and the proposed approach, respectively, on the FA image combined with axial SWI. GPe (The light green) segmented on T₂W combined with SWI (i.e., segmented GPe in Fig. 16 (c)) is incorporated as contours in (b) and (c), respectively, to see overlaps between Pu and GPe. Note that overlaps between Pu and GPe in (c) are considerably reduced (see top right of (b) and (c)).

Fig. 19

Comparison of segmentation results from the single-modality based approaches on T₁W image for CN, Tha, and Pu (dataset 6). The violet, dark green, and cyan represent CN, Tha, and Pu, respectively. Top and bottom in each figure represent contours and volumetric segmentations, respectively. Figures (a), (b), and (c) show segmentation results of GAS, GAS with shape prior using g, and GAS with shape prior using g’ on T₁W image, respectively. Two views of for CN, Tha, and Pu are shown in left and right columns.

Fig. 20

Segmentation results from FSL FIRST and FreeSurfer on T₁W image for CN, Tha, and Pu (dataset 6). The violet, dark green, and cyan represent CN, Tha, and Pu, respectively. Top and bottom in each figure represent contours and volumetric segmentations, respectively. Figures (a) and (b) show segmentation results of FSL FIRST and FreeSurfer, respectively. Two views of for CN, Tha, and Pu are shown in left and right columns.

Fig. 21

Comparison of segmentation results from the multi-modality based approaches on T₁W data combined with FA image, SWI, or T₂W image for CN, Tha and Pu (dataset 6). The violet, dark green, and cyan represent CN, Tha, and Pu, respectively. Top and bottom in each figure represent contours and volumetric segmentations, respectively. First row (i.e., figures (a), (b), and (c)) show segmentation results of GAS. Second row (i.e., figures (d), (e), and (f)) show segmentation results of the proposed approach without non-overlapping constraints. Third row (i.e., figures (g), (h), and (i)) show segmentation results of the proposed approach. First (i.e., figures (a), (d), and (g)), second (i.e., figures (b), (e), and (h)), and third (i.e., figures (c), (f), and (i)) columns represent segmentation results of each approach on T₁W combined FA, SWI, and T₂W, respectively. The left and right sides in each figure are two views of CN, Tha, and Pu. GPe (The light green) segmented on T₂W combined with SWI (i.e., segmented GPe in Fig. 16 (c)) is incorporated as contours in (d)-(i) to see overlaps between Pu and GPe. Note that overlaps between Pu and GPe in figures (g)-(i) are considerably reduced (see top right of (d)-(i))..