MONAI/PyTorch implementation of a 2D U-Net for binary semantic segmentation of fluorescent cochlear inner hair cells in single-channel two-photon microscopy TIFF images.
This model segments inner hair cells (IHCs) from in vivo (Paper) two-photon fluorescence images in which the fluorescent signal primarily labels afferent neurons and their synaptic terminals, rather than the hair cells themselves. It therefore provides IHC segmentation when cell boundaries must be inferred indirectly from the surrounding afferent innervation. .
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.
