Segmentation and deconvolution prediction models for the MEDUSSA pipeline. From the article "Deep-learning-based deconvolution and segmentation of fluorescent membranes for high precision bacterial cell size profiling".

Models for segmentation of membranes in fluorescent images and prediction of deconvolved membranes from either membranes or cytoplasmic fluorescence. Deconvolution prediction models For the FM2FM and FP2FM models, you'll need the csbdeep and TensorFlow Python libraries installed in a conda software environment. The models must first be downloaded and unzipped. Then can be loaded this way in, for example, a Jupyter notebook: from csbdeep.models import CAREfrom csbdeep.utils import normalizemodel = CARE(config=None, name=$MODEL_NAME$, basedir=$MODEL_DIRECTORY$) prediction = model.predict(normalize(image),axes='YX',n_tiles=(4,4)) FM2FM is a CARE-trained TensorFlow model (doi:10.1038/s41592-018-0216-7) for the prediction of deconvolved membranes from non-deconvolved membrane images FP2FM is a CARE-trained TensorFlow model for the prediction of deconvolved membranes from non-deconvolved cytoplasmic fluorescence images Instance segmentation models For the RawFMSeg and FMSeg models, you'll need the omnipose (doi:10.1038/s41592-022-01639-4) Python library installed in a conda software environment. The models must first be downloaded and unzipped. Then can be loaded this way in, for example, a Jupyter notebook: from cellpose_omni import modelsfrom omnipose.utils import normalize99model = models.CellposeModel(pretrained_model=$MODEL_PATH$, nclasses=3, nchan=1,diam_mean=0) masks, flows, styles = model.eval(normalize99(image))Alternatively, if you use the Omnipose GUI, in the "Models" tab you can click on "Add custom torch model to GUI" option and then navigate to the downloaded and unzipped model RawFMSeg is an Omnipose-trained PyTorch model to segment raw fluorescent membranes FMSeg is an Omnipose-trained PyTorch model to segment deconvolved fluorescent membranes