Extended Data Fig. 6: Fine-tuning ACE segmentation models to segment other cellular markers with different morphological features compared to c-Fos. | Nature Methods

Extended Data Fig. 6: Fine-tuning ACE segmentation models to segment other cellular markers with different morphological features compared to c-Fos.

From: A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy

Extended Data Fig. 6

Panel a shows a randomly selected image patch from training data vs. a new unseen dataset with an enlarged view of several cells to highlight the different morphological appearance. b. An axial view from a random depth of the whole brain of the new dataset. c. two randomly selected image patches (with the size of 512x512x512 voxels); patch number 1 was used to fine-tune the ACE UNETR model while patch number 2 was used to evaluate the model performance. d. Qualitative ACE performance before and after fine-tuning. e. Quantitative performance of ACE deep learning models on N: 152 unique patches of 963≃0.17×0.17×0.19 mm3. Box plots: box limits, upper and lower quartiles; center line, median; whiskers, 1.5× interquartile range; points, outliers. Mann-Whitney U test (two-sided), ***p < 0.0001.

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