MONAI/PyTorch implementation of a 2D U-Net for binary semantic segmentation of fluorescent cochlear hair cells in single-channel two-photon microscopy TIFF images.
This model segments 2-photon fluorescence microscopy images containing fluorescently-labelled hair cells in the cochlea.
The model is trained to segment fluorescently-labelled inner hair cells (mostly GCaMP6) in the neonatal cochlea from in-vivo 2-photon imaging experiments (Paper).
The original segmentation was performed with Cellpose and manually revised in napari.
As a result, the algorithm performs better than Cellpose in ignoring cells different from inner hair cells, such as outer hair cells or supporting cells, and in "blurry" in-vivo experiments.
python infer_binary.py input_Avg.tif --output-type both --output prediction_mask.tif --labels-output prediction_labels.tif
Training Data
The model was trained on a microscopy dataset acquired as part of the paper In vivo spontaneous Ca2+ activity in the pre-hearing mammalian cochlea by De Faveri F., Ceriani F. and Marcotti W.
The training dataset consisted of two-photon fluorescence images of GCaMP-labelled cochlear hair cells together with manually curated binary segmentation masks.
Evaluation
Validation Dice:0.787
Limitations
Trained on mouse cochlear two-photon microscopy.
Expected to perform best on images acquired under similar imaging conditions.
Not evaluated on other microscopy modalities or species.
