MRI Deep Learning‐Based Automatic Segmentation of Interventricular Septum for Black‐Blood Myocardial T2* Measurement in Thalassemia

Author:

Lian Zifeng123ORCID,Lu Qiqi123ORCID,Lin Bingquan4,Chen Lingjian5,Peng Peng67ORCID,Feng Yanqiu123ORCID

Affiliation:

1. School of Biomedical Engineering Southern Medical University Guangzhou China

2. Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou China

3. Guangdong‐Hong Kong‐Macao Greater Bay Area Center for Brain Science and Brain‐Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education Southern Medical University Guangzhou China

4. Department of Medical Imaging Center Nanfang Hospital, Southern Medical University Guangzhou China

5. Department of Equipment, Shunde Hospital Southern Medical University (The First People's Hospital of Shunde, Foshan) Foshan China

6. Department of Radiology The First Affiliated Hospital of Guangxi Medical University Nanning China

7. NHC Key Laboratory of Thalassemia Medicine and Guangxi Key Laboratory of Thalassemia Research Nanning China

Abstract

BackgroundThe T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability.PurposeTo develop a deep learning‐based method for automatic septum segmentation from black‐blood MR images for the myocardial T2* measurement of thalassemia patients.Study TypeRetrospective.Population/SubjectsOne hundred forty‐six transfusion‐dependent thalassemia patients with cardiac MR examinations from two centers. Data from Center 1 (1.5 T) were assigned to the training (100 examinations) and internal testing (20 examinations) sets; data from Center 2 were assigned to the external testing set (26 examinations; 10 at 1.5 T and 16 at 3.0 T).Field Strength/Sequence1.5 T and 3.0 T, multiecho gradient‐echo sequence.AssessmentA modified attention U‐Net for septum segmentation was constructed and trained, and its performance evaluated on unseen internal and external datasets. T2* was measured by fitting the average septum signal, separately segmented by automatic and manual methods.Statistical TestsAgreement between manual and automatic septum segmentations was assessed with the Dice coefficient, and T2* agreement was assessed using the Bland–Altman plot and the coefficient of variation (CoV).ResultsThe median Dice coefficient of deep network‐based septum segmentation was 0.90 [0.05] on the internal dataset, 0.82 [0.10] on the external 1.5 T dataset, and 0.86 [0.14] on the external 3.0 T dataset. T2* measurements using automatic segmentation corresponded with those from manual segmentation, with a mean difference of 0.02 (95% LoA: −0.74 to 0.79) msec, 0.43 (95% LoA: −2.1 to 3.0) msec, and 0.36 (95% LoA: −0.72 to 1.4) msec on the three datasets. The CoVs between the two methods were 3.1%, 7.0%, and 6.1% on the internal and two external datasets, respectively.Data ConclusionsThe proposed septum segmentation yielded myocardial T2* measurements which were highly consistent with those obtained by manual segmentation. This automatic approach may facilitate data processing and avoid operator‐dependent variability in practice.Evidence Level4Technical EfficacyStage 1

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

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