Welcome to the Rauschecker-Sugrue Lab at UCSF

Our lab, co-directed by Dr. Andreas Rauschecker and Dr. Leo Sugrue, uses neuroimaging and advanced image analysis methods to better understand the brain in health and disease.

Our work draws from the fields of neuroscience, clinical neuroradiology, psychiatry, electrical engineering, and computer science, especially artificial intelligence (AI) and machine learning.

Example Projects include:

The use of AI for diagnosis, evaluation, and prognosis in neurological disease

The analysis of large datasets for understanding typical and atypical neurodevelopment

The quantification of fetal MRI

Novel image-guided treatments for psychiatric disease

What Are Polygenic Scores and Why Are They Important?

JAMA

Sugrue LP, Desikan RS

Multi-Disease Segmentation of Gliomas and White Matter Hyperintensities in the BraTS Data Using a 3D Convolutional Neural Network.

Frontiers in computational neuroscience

Rudie JD, Weiss DA, Saluja R, Rauschecker AM, Wang J, Sugrue L, Bakas S, Colby JB

Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans.

Radiology

Chen JV, Chaudhari G, Hess CP, Glenn OA, Sugrue LP, Rauschecker AM, Li Y

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Welcome to the Rauschecker-Sugrue Lab at UCSF

Example Projects include:

What Are Polygenic Scores and Why Are They Important?

Multi-Disease Segmentation of Gliomas and White Matter Hyperintensities in the BraTS Data Using a 3D Convolutional Neural Network.

Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans.