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

• Published in: International journal of surgery case reports Vol. 111; p. 108818
• Main Authors: Mohammadi, Sana, Ghaderi, Sadegh, Ghaderi, Kayvan, Mohammadi, Mahdi, Pourasl, Masoud Hoseini
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2023; Elsevier
• Subjects: Case Series; Marker-controlled watershed algorithm; Meningioma; MRI; Tumor segmentation; T1-w; CE; CNN; MRI; Marker-controlled watershed algorithm; DL; KNN; SVM; ROI; Tumor segmentation; FCM; DSC; MCWS; Meningioma; ML
• ISSN: 2210-2612; 2210-2612
• DOI: 10.1016/j.ijscr.2023.108818

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. 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. 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. 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. •Successful segmentation of meningiomas using automated marker-controlled watershed and fuzzy c-means algorithms.•High sensitivity (0.8822) in detecting meningioma tumors compared to manual segmentation.•Good overlap between Otsu's thresholding and fuzzy c-means clustering segmentation (Dice coefficient: 0.6599).