ArticlebioRxiv : the preprint server for biology2024
A Deep Learning Model for Accurate Segmentation of the
Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The use of microcomputed tomography (Micro-CT) for imaging biological samples has burgeoned in the past decade, due to increased access to scanning platforms, ease of operation, isotropic three-dimensional image information, and the ability to derive accurate quantitative data. However, manual data analysis of Micro-CT images can be laborious and time intensive. Deep learning offers the ability to streamline this process, but historically has included caveats-namely, the need for a large amount of training data, which is often limited in many Micro-CT studies. Here we show that accurate deep learning models can be trained using only 1-3 Micro-CT images of the adult Summary: Micro-CT data can be automatically segmented and quantified using a deep learning model trained on as few as 3 samples, facilitating rapid comparison of developmental phenotypes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.