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Abstract Automatic segmentation of murine lungs is an integral part of the preclinical research pipeline. Being able to quickly and reliably segment the lungs across hundreds of samples is essential for extracting metrics of interest to study certain pathologies. However, the deep learning (DL) approaches proposed in the literature for this task are trained on homogeneous datasets, limiting their generalizability.
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