Abstract Background: Idiopathic pulmonary fibrosis significantly threatens patient survival and remains a condition with limited effective treatment options. There is an urgent need to expedite the exploration of idiopathic pulmonary fibrosis mechanisms and identify suitable therapeutic approaches. Non-invasive and rapid segmentation of lung tissue, coupled with fibrosis quantification, is essential for drug development and efficacy monitoring.Material and Methods:A total of 59 mice were divided into training, validation and test sets according to the ratio of 70%:15%:15%. Based on this ratio, we performed a six-fold cross-validation to ensure the reliability of our results and calculated the average performance across all test sets. At first, a 2.5D UNet was utilized to segment the lung tissue of mice, followed by the calculation of a fibrosis score based on the segmented output, which can be used to evaluate the degree of pulmonary fibrosis in mice. Dice score, precision and recall are used to evaluated the performance of 2.5D UNet.Results: The 2.5D UNet yielded promising results in segmenting mice lung tissue. In the test set, the 2.5D UNet achieved an average Dice score of 0.938, precision of 0.941, and recall of 0.936 across the six-fold cross-validation. The fibrosis score effectively demonstrated the varying impacts of different modeling or treatment methods.Conclusions:The 2.5D UNet can effectively segment mice lung tissue and evaluate fibrosis scores, which lays a solid foundation for further research.
Zheng et al. (Thu,) studied this question.