Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline

Sun，Fuhai1; Lyu，Junyan1; Jian，Si2; Qin，Yuanyuan2; Tang，Xiaoying1,3

2023

10.1007/s00330-023-09979-1

European Radiology   影响因子和分区

0938-7994

1432-1084

Objectives: This study aims at a fully automatic pipeline for measuring the magnetic resonance parkinsonism index (MRPI) using deep learning methods. Methods: MRPI is defined as the product of the pons area to the midbrain area ratio and the middle cerebellar peduncle (MCP) width to the superior cerebellar peduncle (SCP) width ratio. In our proposed pipeline, we first used nnUNet to segment the brainstem and then employed HRNet to identify two key boundary points so as to sub-divide the whole brainstem into midbrain and pons. HRNet was also employed to predict the MCP endpoints for measuring the MCP width. Finally, we segmented the SCP on an oblique coronal plane and calculated its width. A total of 400 T1-weighted magnetic resonance images (MRIs) were used to train the nnUNet and HRNet models. Five-fold cross-validation was conducted to evaluate our proposed pipeline’s performance on the training dataset. We also evaluated the performance of our proposed pipeline on three external datasets. Two of them had two raters manually measuring the MRPI values, providing insights into automatic accuracy versus inter-rater variability. Results: We obtained average absolute percentage errors (APEs) of 17.21%, 18.17%, 20.83%, and 22.83% on the training dataset and the three external validation datasets, while the inter-rater average APE measured on the first two external validation datasets was 11.31%. Our proposed pipeline significantly improved the MRPI quantification accuracy over a representative state-of-the-art traditional approach (p < 0.001). Conclusion: The proposed automatic pipeline can accurately predict MRPI that is comparable with manual measurement. Clinical relevance statement: This study presents an automated magnetic resonance parkinsonism index measurement tool that can analyze large amounts of magnetic resonance images, enhance the efficiency of Parkinsonism-Plus syndrome diagnosis, reduce the workload of clinicians, and minimize the impact of human factors on diagnosis. Key Points: • We propose an automatic pipeline for measuring the magnetic resonance parkinsonism index from magnetic resonance images. • The effectiveness of the proposed pipeline is successfully established on multiple datasets and comparisons with inter-rater measurements. • The proposed pipeline significantly outperforms a state-of-the-art quantification approach, being much closer to ground truth.

Anatomic landmarks Artificial intelligence Deep learning Magnetic resonance imaging

SCI

英语

第一 ; 通讯

Radiology, Nuclear Medicine & Medical Imaging

Radiology, Nuclear Medicine & Medical Imaging

WOS:001079198900001

SPRINGER

CLINICAL MEDICINE

2-s2.0-85165915517

Scopus

1.Department of Electronic and Electrical Engineering,College of Engineering,Southern University of Science and Technology,Shenzhen,Xili, Nanshan,518055,China
2.Department of Radiology,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan,Jiefang Avenue,430030,China
3.Jiaxing Research Institute,Southern University of Science and Technology,Jiaxing,China

Sun，Fuhai,Lyu，Junyan,Jian，Si,et al. Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline[J]. European Radiology,2023,33(12):8844-8853.

Sun，Fuhai,Lyu，Junyan,Jian，Si,Qin，Yuanyuan,&Tang，Xiaoying.(2023).Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline.European Radiology,33(12),8844-8853.

Sun，Fuhai,et al."Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline".European Radiology 33.12(2023):8844-8853.