Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

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© Springer Nature Switzerland AG 2018. We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variabili...

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Bibliographic Details
Main Authors: Pace, Danielle Frances, Dalca, Adrian Vasile, Brosch, Tom, Geva, Tal, Powell, Andrew J., Weese, Jürgen, Moghari, Mehdi H., Golland, Polina
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:English
Published: Springer International Publishing 2022
Online Access:https://hdl.handle.net/1721.1/137463.2

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https://hdl.handle.net/1721.1/137463.2

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