Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation

Abstract

The segmentation from MRI of macroscopically ill-defined and highly variable structures, such as the hippocampus (Hc) and the amygdala (Am), requires the use of specific constraints. Here, we describe and evaluate a fast fully automatic hybrid segmentation that uses knowledge derived from probabilistic atlases and anatomical landmarks, adapted from a semi-automatic method. The algorithm was designed at the outset for application on images from healthy subjects and patients with hippocampal sclerosis. Probabilistic atlases were built from 16 healthy subjects, registered using SPM5. Local mismatch in the atlas registration step was automatically detected and corrected. Quantitative evaluation with respect to manual segmentations was performed on the 16 young subjects, with a leave-one-out strategy, a mixed cohort of 8 controls and 15 patients with epilepsy with variable degrees of hippocampal sclerosis, and 8 healthy subjects acquired on a 3 T scanner. Seven performance indices were computed, among which error on volumes RV and Dice overlap K. The method proved to be fast, robust and accurate. For Hc, results with the new method were: 16 young subjects {RV=5%, K=87%}; mixed cohort {RV=8%, K=84%}; 3 T cohort {RV=9%, K=85%}. Results were better than with atlas-based (thresholded probability map) or semi-automatic segmentations. Atlas mismatch detection and correction proved efficient for the most sclerotic Hc. For Am, results were: 16 young controls {RV=7%, K=85%}; mixed cohort {RV=19%, K=78%}; 3 T cohort {RV=10%, K=77%}. Results were better than with the semi-automatic segmentation, and were also better than atlas-based segmentations for the 16 young subjects.

Fig. 5

Measure of actual atlas mismatch (Y-axis) as a function of detection tests (X-axis: z-score of the test value with respect to S1–S16): (a) global mismatch (measure = mean symmetric distance between the 0.5-level object and the manual segmentation); (b) atrophy mismatch (measure = relative error on volumes between the 0.5-level object and the manual segmentation); (c) initial object mismatch (measure = false positives ratio between the initial object and the manual segmentation). The solid vertical line indicates the threshold value and the dashed line indicates the median between the average value and the threshold. All the points in the green area are those for which mismatch is detected.

Fig. 6

Definition of the probabilistic atlas constraints C1, C2, C3, and C4. (a and b) Histogram of the area corresponding to voxels which are inside (true positives, TP) and outside (false positives, FP) the manual segmentation as a function of IPA_O(v), for the leave-one-out atlases, for S1–S16 (a. for Hc and b. for Am). (c and d) probability zones (PZ) used in the 4 levels of probabilistic atlas constraint (C1-C4) evaluated for the method, on an axial slice (c. for Am and d. for Hc). (e) Sensitivity to γ^PZ parameters (RV and K for Hc (in red) and Am (in green) when varying γ^PZ) (see text for details). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 7

Quantitative indices for S1–S16 with the leave-one-out atlas when varying parameters. a and b when varying ratios controlling intensity parameters with respect to grey matter intensity estimate. (a) the ratios for average intensity (Y-axis) and tolerance (X-axis) are let varying for Hc, respectively between 0.9 and 1.1 (default = 1.0) and 1.6 and 2 (default 1.8). (b) the ratios for average intensity (Y-axis) and tolerance (X-axis) are let varying for Am, respectively between 0.8 and 1.0 (default = 0.9) and 0.9 and 1.3 (default 1.1). (c) when varying initialisation parameters for both structures at the same time (Y-axis: probability between 0.5 and 1 (default = 1); X-axis: erosion structuring element between 0 and 2 (default = 1)).

Fig. 1

Automatic initialisation illustrated on subject S1. (a) Bounding box extraction, on sagittal, coronal and axial sections. (b) Probabilistic atlas, maximal probability zone (IPA_O(v) = 1) obtained by thresholding and regularised thresholding and initial object, for Hc and Am, on two representative axial slices (one per row).

Fig. 2

Segmentation results for group 1: 3D-renderings of automatic and manual segmentations, and overlap between segmentations (manual segmentations in shades of grey) and probabilistic atlases on a sagittal slice for the best and worst results (cases S3R and S16R respectively).

Fig. 3

Segmentation results for the patients with Hc sclerosis in group 2: 3D-renderings of automatic and manual segmentations, and overlap between segmentations (manual segmentations in shades of grey) and probabilistic atlases on a sagittal slice for the best and worst results (cases HS8R and HS5L respectively).

Fig. 4

Segmentation results for group 3: 3D-renderings of automatic and manual segmentations, and overlap between segmentations (manual segmentations in shades of grey) and probabilistic atlases on a sagittal slice for the best and worst results (cases NC3T2L and NC3T4L respectively) and NC3T7L, for which atlas mismatch is detected.