Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm

Sana Mohammadi; Sadegh Ghaderi; Kayvan Ghaderi; Mahdi Mohammadi; Masoud Hoseini Pourasl

doi:10.1016/j.ijscr.2023.108818

Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm

Int J Surg Case Rep. 2023 Oct:111:108818. doi: 10.1016/j.ijscr.2023.108818. Epub 2023 Sep 13.

Authors

Sana Mohammadi¹, Sadegh Ghaderi², Kayvan Ghaderi³, Mahdi Mohammadi⁴, Masoud Hoseini Pourasl⁵

Affiliations

¹ Department of Medical Sciences, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
² Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: s_ghaderi@razi.tums.ac.ir.
³ Department of Information Technology and Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj 66177-15175, Iran.
⁴ Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
⁵ Department of Radiology, Kurdistan University of Medical Sciences, Sanandaj, Iran.

Abstract

Introduction and importance: Accurate segmentation of meningiomas from contrast-enhanced T1-weighted (CE T1-w) magnetic resonance imaging (MRI) is crucial for diagnosis and treatment planning. Manual segmentation is time-consuming and prone to variability. To evaluate an automated segmentation approach for meningiomas using marker-controlled watershed segmentation (MCWS) and fuzzy c-means (FCM) algorithms.

Case presentation and methods: CE T1-w MRI of 3 female patients (aged 59, 44, 67 years) with right frontal meningiomas were analyzed. Images were converted to grayscale and preprocessed with Otsu's thresholding and FCM clustering. MCWS segmentation was performed. Segmentation accuracy was assessed by comparing automated segmentations to manual delineations.

Clinical discussion: The approach successfully segmented meningiomas in all cases. Mean sensitivity was 0.8822, indicating accurate identification of tumors. Mean Dice similarity coefficient between Otsu's and FCM1 was 0.6599, suggesting good overlap between segmentation methods.

Conclusion: The MCWS and FCM approach enables accurate automated segmentation of meningiomas from CE T1-w MRI. With further validation on larger datasets, this could provide an efficient tool to assist in delineating meningioma boundaries for clinical management.

Keywords: MRI; Marker-controlled watershed algorithm; Meningioma; Tumor segmentation.