Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg

Abstract

Background and purpose: To develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg.

Materials and methods: This retrospective study included 60 subjects [30 Alzheimer's disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference.

Results: The single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects.

Conclusion: Our deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.

Keywords: artificial intelligence; brain; deep learning; intracranial volume segmentation; neurodegenerative disease.

Figure 1

Patient flow diagram of this retrospective cohort. AD, Alzheimer’s disease; MCI, Mild cognitive impairment; CN, Cognitively normal; ADNI, Alzheimer’s Disease Neuroimaging Initiative.

Figure 2

Model architecture of the proposed deep learning-based ICV segmentation model. The model exploits the basic 2D UNet architecture, which consists of five encoder and four decoder layers. Conv 3 × 3, convolutional layer with a kernel size of 3 × 3; BatchNorm, batch normalization; ReLU, rectified linear unit; MaxPool, maxpooling.

Figure 3

Scatterplot of the correlation between the manually segmented ICV from the T1-weighted images in the test set and automated segmented ICV determined by our proposed model trained with scaled T1-weighted images only (A), unscaled both T1-weighted and T2-FLAIR images (B), FreeSurfer (C), NeuroQuant (D), and SynthSeg (E).

Figure 4

Our multimodality deep learning model shows robustness in various protocols and patient ages. The multimodal model (right) shows advantages in enhanced T1-weighted images around hyperintense enhanced vessels (A) and images from a young patient without atrophy images (B) compared with the single-modality model (middle).