Frequently Asked Questions

DenseNet (Densely Connected Convolutional Network) is a convolutional neural network architecture designed for image classification. Its key idea is that every layer is directly connected to every other layer within a dense block: the input to each layer is the concatenation of the feature maps from all preceding layers, not just the previous one.

This dense connectivity has two practical benefits:

• Feature reuse - early low-level features (edges, textures) remain accessible to later layers without having to be re-learned, so the network extracts richer representations with fewer parameters.
• Gradient flow - during training, gradients can travel directly from the loss back to the earliest layers, which prevents the “vanishing gradient” problem and makes training more stable, especially on small datasets.

In Mycol, DenseNet is applied at the individual cell level: each segmented cell patch is classified into one of your user-defined classes. The model is either used with a pre-trained checkpoint you upload, or fine-tuned from scratch on your own annotated cell patches.

When Mycol measures a cell - its area, perimeter, diameter, and so on - the raw number is always in pixels, because that is what the image is made of. A pixel has no inherent physical size; it depends entirely on your microscope’s magnification and camera settings.

The pixel-to-distance (px → µm) conversion lets you express measurements in real-world units by supplying a single number: how many micrometres one pixel represents in your images (the pixel size or image scale, often found in your microscope software or image metadata).

For example:

• If 1 pixel = 0.5 µm and a cell has an area of 400 px², its physical area is 400 × 0.5² = 100 µm².
• Linear measurements (perimeter, diameter) scale by the factor directly: 40 px × 0.5 = 20 µm.

If no conversion factor is provided, Mycol simply reports all measurements in pixels. You can still compare cells within the same experiment, but cross-experiment or publication comparisons require consistent physical units.

SAM2 (Segment Anything Model 2) is a general-purpose segmentation model developed by Meta AI. Unlike Cellpose, which was trained specifically on microscopy images, SAM2 was trained on an enormous and diverse collection of natural images and videos. Its strength is promptable segmentation: given a point, bounding box, or rough outline supplied by the user, SAM2 predicts a precise mask for the object at that location.

In Mycol, SAM2 is used as an interactive segmentation tool. Instead of running fully automatically across the whole image, you highlight a cell with a box and SAM2 will generate a high-quality mask for that cell. This makes it especially useful for:

• Segmenting unusual cell types or objects where Cellpose may struggle.
• Quickly annotating small numbers of cells for a training set without running a full automatic pass.

Because SAM2 relies on your prompt rather than learning from your specific image type, it does not need to be fine-tuned - it works out of the box. However, for fully automatic, hands-off segmentation of large image sets, a trained Cellpose model will generally be faster and more consistent.

Images (required)

The microscopy or sample images you want to analyse. Standard formats such as TIFF, PNG, and JPEG are supported. Every other file type is optional and supplementary - images are the only thing you must upload to start working. Without an associated image, a mask will not be recognised when uploaded.

Masks (optional)

Segmentation masks that outline the cells or regions of interest in your images. Each mask is a labelled image where every cell is represented by a unique integer ID, and background pixels are zero. Uploading existing masks lets you skip the automatic segmentation step and jump straight to classification or analysis, or to continue refining masks you created in a previous session. You can upload the Cellpose .npy format or a tiff where each cell is a different integer value.

Hyperparameter CSV (optional)

A comma-separated file that specifies the seared hyperparameter space during Cellpose training. The hyperparamters can significantly affect model performanace so setting Cellpose hyperparameters in the Annotation page can improve your results. By uploading the searched hyperparameter csv, you can automatically set the hyperparameters to the optimal values each session.

Saved Session (optional)

A zip archive produced by the Session Restore download option. Uploading it reloads a previous Mycol session in full: all images, masks, class labels, trained models, and settings are restored exactly as they were when the session was saved. This is the recommended way to pause and resume long annotation or training projects, or to share a complete working state with a collaborator.

You do not need a machine learning background to train useful models in Mycol. The guidelines below cover the most impactful things you can do to get good results.

Start with the pre-trained model

Before training anything, run the default Cellpose or DenseNet model on a few images. Often the out-of-the-box result is already good enough, or needs only minor corrections. Training is worth the effort only when automatic results are consistently poor.

Data quality beats data quantity

A small set of carefully checked annotations is almost always better than a large set of sloppy ones. For Cellpose, 10–30 well-corrected images are usually enough to see a meaningful improvement. For DenseNet classification, aim for at least 50–100 annotated cells per class, and ensure the classes are balanced (similar numbers per class).

Cover the diversity in your data

Training images should represent the full range of conditions you will encounter: different brightness levels, focal planes, cell densities, and edge cases. A model trained only on the “easy” images will struggle with the harder ones.

Watch the loss curves

After training, Mycol shows a plot of how the model’s error (the “loss”) decreased over each training epoch. A healthy training run shows both the training loss and the validation loss falling steadily and then levelling off close together. If the validation loss starts rising while the training loss keeps falling, the model is overfitting - it is memorising your training data rather than learning general patterns. In that case, stop training earlier (fewer epochs) or add more diverse training images.

Iterate in small steps

Train a model, test it on new images, correct the errors, add those corrected images back to your training set, and re-train. This cycle of annotate → train → correct → repeat is the most efficient way to improve a model without needing thousands of images up front.

For every segmented cell, Mycol computes a set of shape descriptors from the mask region. All values are reported in pixels unless a pixel-to-distance conversion factor is supplied. Descriptors are computed using skimage.measure.regionprops from the scikit-image library.