A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI.
Purpose: Magnetic resonance imaging (MRI) is an essential technique for diagnosing pituitary adenomas; however, it is also challenging for neurosurgeons to use it to precisely identify some types of microadenomas. A novel neural network model was developed using preoperative MRI to assist clinicians...
| Publicado en: | Neuroradiology Vol. 67; no. 4; pp. 1061 - 1071 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184825695&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184825695 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Apr2025 vid: 67 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184825695 184223204 184825695 184825695 10.1007/s00234-025-03599-w 184825695 ppf: 1061 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI. aug: au: Yuan, ChenGang Qu, Hang Dai, HuMing Jiang, HaiXiao Cao, DeMao Shao, LiYing Zhou, LiangXue Peng, AiJun affil: https://ror.org/03tqb8s11 Department of Neurosurgery, Affiliated Hospital of Yangzhou University, Yangzhou University, 225000, Yangzhou, Jiangsu Province, China sug: subj: Adenoma Diagnosis Pituitary Neoplasms Diagnosis Adenoma Surgery Pituitary Neoplasms Surgery Preoperative Care Magnetic Resonance Imaging Methods Neural Networks (Computer) Models, Statistical Semantics Human Kruskal-Wallis Test Descriptive Statistics Data Analysis Software Male Female Adult Middle Age Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Purpose: Magnetic resonance imaging (MRI) is an essential technique for diagnosing pituitary adenomas; however, it is also challenging for neurosurgeons to use it to precisely identify some types of microadenomas. A novel neural network model was developed using preoperative MRI to assist clinicians in diagnosing pituitary microadenomas. Method: Sixty patients with pathologically diagnosed pituitary microadenomas, including hyperprolactinemia (n = 19), growth hormone microadenomas (n = 17), and adrenocorticotropin microadenomas (n = 24), were enrolled. An image edge-supervised same receptive field semantic segmentation network was developed based on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted images. Results: The mean Intersection over Unions of our neural network model were 0.7013 ± 0.3400, 0.7295 ± 0.321, and 0.8053 ± 0.3052 for the test sets of T1-weighted, T2-weighted, and contrast-enhanced T1-weighted sequences, respectively, while the Dice Similarity Coefficient values were 0.8075 ± 0.3895, 0.8192 ± 0.3733, and 0.8860 ± 0.3443 for the corresponding sequences. The performance on contrast-enhanced T1-weighted images was better than that of the other two MR sequences. Conclusions: The image edge-supervised same receptive field segmentation network can potentially be used to precisely identify pituitary microadenomas automatically with preoperative MRI. The developed model exhibited good performance with contrast-enhanced T1-weighted images and could help neurosurgeons accurately determine the locations of pituitary microadenomas. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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