Abstract #2826

Segmentation of Diffuse White Matter Abnormality in Preterm Infants using Deep Learning

Hailong Li¹, Nehal A. Parikh^1,2, Jinghua Wang³, Stephanie Merhar^1,2, Ming Chen⁴, Milan Parikh¹, Scott Holland^5,6, and Lili He^1,2

¹Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States, ²Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States, ³Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States, ⁴Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH, United States, ⁵Medpace Inc, Cincinnati, OH, United States, ⁶Physics, University of Cincinnati, Cincinnati, OH, United States

Diffuse white matter abnormality (DWMA) is observed in 50-80% of very preterm infants at term-equivalent age. Despite autopsy studies showing correlation with neuropathology, the relationship of DWMA with long term neurodevelopmental impairments remains controversial. The controversy may be due to the qualitative nature of previous studies of DWMA, likely resulting in measurement error and perhaps contributing to the lack of association with neurodevelopmental impairments in some studies. In this study, we developed a deep learning approach to objectively and automatically segment DWMA regions on T2-weighted MRI images. The internal and external validations demonstrated very accurate and reproducible DWMA segmentation performance.

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