Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.

Journal: BMC medical imaging
Published Date: Mar 5, 2024

Abstract

BACKGROUND: This study aimed to establish a dedicated deep-learning model (DLM) on routine magnetic resonance imaging (MRI) data to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Another purpose of our work was to develop a radiomics model based on the radiomics features extracted from automatic segmentation to differentiate low- and high-grade meningiomas before surgery.

Authors

  • Liping Yang
    Department of Emergency, The First People's Hospital of Lianyungang, Lianyungang City, 222002, China.
  • Tianzuo Wang
    Medical Imaging Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China. agntwz@126.com.
  • Jinling Zhang
    Medical Imaging Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
  • Shi Kang
    Medical Imaging Department, The Second Hospital of Heilongjiang Province, Harbin, China.
  • Shichuan Xu
    Department of Medical Instruments, Second Hospital of Harbin, Harbin, 150001, China. 69744792@qq.com.
  • Kezheng Wang
    Department of PET-CT, Harbin Medical University Cancer Hospital, Harbin, 150001, China. wangkezheng9954001@163.com.

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