IMFSegNet: Cost-effective and objective quantification of intramuscular fat in histological sections by deep learning.

ABSTRACT: The assessment of muscle condition is of great importance in various research areas. In particular, evaluating the degree of intramuscular fat (IMF) in tissue sections is a challenging task, which today is still mostly performed qualitatively or quantitatively by a highly subjective and error-prone manual analysis. We here realize the mission to make automated IMF analysis possible that (i) minimizes subjectivity, (ii) provides accurate and quantitative results quickly, and (iii) is cost-effective using standard hematoxylin and eosin (H&E) stained tissue sections. To address all these needs in a deep learning approach, we utilized the convolutional encoder-decoder network SegNet to train the specialized network IMFSegNet allowing to accurately quantify the spatial distribution of IMF in hi

SUBMITTER: Praetorius JP 

PROVIDER: S-EPMC10407270 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature