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논문 기본 정보

자료유형
학술저널
저자정보
Yun Seok Seo (Department of Radiology Seoul National University Children’s Hospital Seoul Korea) Young Hun Choi (Department of Radiology Seoul National University Children’s Hospital Seoul National University College of Medicine Seoul Korea) Joon Sung Lee (GE Healthcare Korea Seoul Korea) Seul Bi Lee (Department of Radiology Seoul National University Children’s Hospital Seoul National University College of Medicine Seoul Korea) Yeon Jin Cho (Department of Radiology Seoul National University Children’s Hospital Seoul National University College of Medicine Seoul Korea) Seunghyun Lee (Department of Radiology Seoul National University Children’s Hospital Seoul National University College of Medicine Seoul Korea) Su-Mi Shin (Department of Radiology SMG-SNU Boramae Medical Center Seoul Korea) Jung-Eun Cheon (Department of Radiology Seoul National University Children’s Hospital Seoul National University College of Medicine Institute of Radiation Medicine Seoul National University Medical Research Center Se)
저널정보
대한자기공명의과학회 Investigative Magnetic Resonance Imaging Investigative Magnetic Resonance Imaging 제27권 제1호
발행연도
2023.3
수록면
42 - 48 (7page)

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초록· 키워드

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Purpose: To develop and evaluate a deep learning technique to automatically segment bone structures in zero echo time (ZTE) for skull magnetic resonance imaging (MRI) in children. Materials and Methods: From January to December 2021, 38 bone ZTE MRIs from infants and children (age range, 1–31 months) were collected for model development. Mask images were generated by manually segmenting the craniofacial bone using a commercial segmentation program. Among them, 35 ZTE series were used to train the three-dimensional (3D)-nnUnet deep learning model and the remaining three series were used for model validation. A temporally different dataset of 19 ZTE bone MRIs obtained in May 2022 from infants and children (age range, 3–168 months) was used to determine the model’s performance. Dice similarity coefficient was calculated for each test case. From 3D volume rendering images, segmentation accuracy, overall image quality, and visibility of cranial sutures were subjectively evaluated on a 5-point scale and compared with ground truth data from manual segmentation. Reasons for segmentation failure were analyzed using axially segmented ZTE images. Results: For the test set, the mean Dice similarity coefficient was 0.985 ± 0.019. The segmentation accuracy was lower than the ground truth without showing a statistically significant difference between the two (3.39 ± 1.11 vs. 3.73 ± 0.77, p = 0.055). The overall image quality and suture visibility showed no significant difference (3.34 ± 0.75 vs. 3.42 ± 0.69, p = 0.317; 3.55 ± 0.97 vs. 3.60 ± 0.95, p = 0.157). Common reasons for low segmentation accuracy were well-pneumatized sinuses, metal artifacts, skin at the vertex level, and bones too thin. Conclusion: The deep learning-based automatic segmentation technique of bone ZTE MRIs showed comparable segmentation performance to manual segmentation. Using the deep learning-based segmentation results, acceptable 3D-volume rendering images of craniofacial bones were generated.
#Zero TE (ZTE) #MRI #Skull #Deep learning

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