Automatic Ventriculomegaly Detection in Fetal Brain MRI: A Step-by-Step Deep Learning Model for Novel 2D-3D Linear Measurements

• Published in: Diagnostics (Basel) Vol. 13; no. 14; p. 2355
• Main Authors: Vahedifard, Farzan, Ai, H Asher, Supanich, Mark P, Marathu, Kranthi K, Liu, Xuchu, Kocak, Mehmet, Ansari, Shehbaz M, Akyuz, Melih, Adepoju, Jubril O, Adler, Seth, Byrd, Sharon
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2023; MDPI
• Subjects: Algorithms; artificial intelligence; Automation; Deep learning; fetal brain; Fetuses; Localization; Magnetic resonance imaging; Measurement; Medical imaging; MRI; U-Net; Ultrasonic imaging; ventriculomegaly; United States > US; deep learning; ventriculomegaly; MRI; fetal brain; U-Net; artificial intelligence
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13142355

In this study, we developed an automated workflow using a deep learning model (DL) to measure the lateral ventricle linearly in fetal brain MRI, which are subsequently classified into normal or ventriculomegaly, defined as a diameter wider than 10 mm at the level of the thalamus and choroid plexus. To accomplish this, we first trained a UNet-based deep learning model to segment the brain of a fetus into seven different tissue categories using a public dataset (FeTA 2022) consisting of fetal T2-weighted images. Then, an automatic workflow was developed to perform lateral ventricle measurement at the level of the thalamus and choroid plexus. The test dataset included 22 cases of normal and abnormal T2-weighted fetal brain MRIs. Measurements performed by our AI model were compared with manual measurements performed by a general radiologist and a neuroradiologist. The AI model correctly classified 95% of fetal brain MRI cases into normal or ventriculomegaly. It could measure the lateral ventricle diameter in 95% of cases with less than a 1.7 mm error. The average difference between measurements was 0.90 mm in AI vs. general radiologists and 0.82 mm in AI vs. neuroradiologists, which are comparable to the difference between the two radiologists, 0.51 mm. In addition, the AI model also enabled the researchers to create 3D-reconstructed images, which better represent real anatomy than 2D images. When a manual measurement is performed, it could also provide both the right and left ventricles in just one cut, instead of two. The measurement difference between the general radiologist and the algorithm ( = 0.9827), and between the neuroradiologist and the algorithm ( = 0.2378), was not statistically significant. In contrast, the difference between general radiologists vs. neuroradiologists was statistically significant ( = 0.0043). To the best of our knowledge, this is the first study that performs 2D linear measurement of ventriculomegaly with a 3D model based on an artificial intelligence approach. The paper presents a step-by-step approach for designing an AI model based on several radiological criteria. Overall, this study showed that AI can automatically calculate the lateral ventricle in fetal brain MRIs and accurately classify them as abnormal or normal.