Dataset Information

Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.

ABSTRACT: OBJECTIVE:To develop an automated vessel wall segmentation method using convolutional neural networks to facilitate the quantification on magnetic resonance (MR) vessel wall images of patients with intracranial atherosclerotic disease (ICAD). METHODS:Vessel wall images of 56 subjects were acquired with our recently developed whole-brain three-dimensional (3-D) MR vessel wall imaging (VWI) technique. An intracranial vessel analysis (IVA) framework was presented to extract, straighten, and resample the interested vessel segment into 2-D slices. A U-net-like fully convolutional networks (FCN) method was proposed for automated vessel wall segmentation by hierarchical extraction of low- and high-order convolutional features. RESULTS:The network was trained and validated on 1160 slices and tested on 545 slices. The proposed segmentation method demonstrated satisfactory agreement with manual segmentations with Dice coefficient of 0.89 for the lumen and 0.77 for the vessel wall. The method was further applied to a clinical study of additional 12 symptomatic and 12 asymptomatic patients with >50% ICAD stenosis at the middle cerebral artery (MCA). Normalized wall index at the focal MCA ICAD lesions was found significantly larger in symptomatic patients compared to asymptomatic patients. CONCLUSION:We have presented an automated vessel wall segmentation method based on FCN as well as the IVA framework for 3-D intracranial MR VWI. SIGNIFICANCE:This approach would make large-scale quantitative plaque analysis more realistic and promote the adoption of MR VWI in ICAD management.

SUBMITTER: Shi F

PROVIDER: S-EPMC6788976 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.

Shi Feng F Yang Qi Q Guo Xiuhai X Qureshi Touseef Ahmad TA Tian Zixiao Z Miao Huijuan H Dey Damini D Li Debiao D Fan Zhaoyang Z

IEEE transactions on bio-medical engineering 20190201 10

<h4>Objective</h4>To develop an automated vessel wall segmentation method using convolutional neural networks to facilitate the quantification on magnetic resonance (MR) vessel wall images of patients with intracranial atherosclerotic disease (ICAD).<h4>Methods</h4>Vessel wall images of 56 subjects were acquired with our recently developed whole-brain three-dimensional (3-D) MR vessel wall imaging (VWI) technique. An intracranial vessel analysis (IVA) framework was presented to extract, straight ...[more]

PMID: 30716027

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Project description:PurposeTo develop a fast and accurate convolutional neural network based method for segmentation of thalamic nuclei.MethodsA cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on conventional and white-matter-nulled (WMn) magnetization prepared rapid gradient echo (MPRAGE) data. A single network was optimized to work with images from healthy controls and patients with multiple sclerosis (MS) and essential tremor (ET), acquired at both 3 T and 7 T field strengths. WMn-MPRAGE images were manually delineated by a trained neuroradiologist using the Morel histological atlas as a guide to generate reference ground truth labels. Dice similarity coefficient and volume similarity index (VSI) were used to evaluate performance. Clinical utility was demonstrated by applying this method to study the effect of MS on thalamic nuclei atrophy.ResultsSegmentation of each thalamus into twelve nuclei was achieved in under a minute. For 7 T WMn-MPRAGE, the proposed method outperforms current state-of-the-art on patients with ET with statistically significant improvements in Dice for five nuclei (increase in the range of 0.05-0.18) and VSI for four nuclei (increase in the range of 0.05-0.19), while performing comparably for healthy and MS subjects. Dice and VSI achieved using 7 T WMn-MPRAGE data are comparable to those using 3 T WMn-MPRAGE data. For conventional MPRAGE, the proposed method shows a statistically significant Dice improvement in the range of 0.14-0.63 over FreeSurfer for all nuclei and disease types. Effect of noise on network performance shows robustness to images with SNR as low as half the baseline SNR. Atrophy of four thalamic nuclei and whole thalamus was observed for MS patients compared to healthy control subjects, after controlling for the effect of parallel imaging, intracranial volume, gender, and age (p < 0.004).ConclusionThe proposed segmentation method is fast, accurate, performs well across disease types and field strengths, and shows great potential for improving our understanding of thalamic nuclei involvement in neurological diseases.

Dataset Information

Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.

Publications

Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.

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