If you don’t do this, the trained model can be confused as to which pixels belong to objects and which belong to the background. you must label all the objects in your chosen training images, even if they are only partially visible. Sparse labelling is not supported at this point, i.e. by using backbone='unet'.ĭo I have to annotate all nuclei (objects) in a training image? What about those that are only partially visible? What about other objects not of interest? ¶ Furthermore, you can also slightly increase the receptive field by changing the backbone in the configuration to a U-Net, i.e. Besides downscaling your input images, you can also change the grid parameter as mentioned above, but do not increase it for Z if you have strongly anisotropic images with relatively few axial planes, e.g. This is similar for StarDist 3D, although the receptive field for the default network configuration is only roughly 35 pixels. Grid values of 4 and even 8 do make sense for images with a large minimum object size, e.g. setting grid=(2,2) will roughly double the receptive field). If your objects are larger than this and the segmentation results indicate over-segmentation, you can either a) downscale your input images such that the object size becomes smaller, or b) increase the receptive field of a StarDist model by changing the grid parameter in the model configuration (e.g. The maximal size of objects that can be well segmented depends on the receptive field of the neural network used inside a StarDist model.įor the default StarDist 2D network configuration, this is roughly 90 pixels. Is there an upper size limit for objects to be well segmented? ¶ For example, you can annotate image crops of 300x300 pixels and then use a patch size of 256x256 pixels for training. To be on the safe side, ensure that the patch size is divisible by 16 along all dimensions. For example, the patch size used for training StarDist must be smaller or equal than the size of the smallest annotated training image. The “patch size” is an important parameter for training StarDist, and the size of images used for training affects what an appropriate value for the patch size should be (to maintain compatibility with the neural network architecture). Example: if you have small cells with a diameter of 20 pixels, it might be sufficient to have annotated images of size 160x160, whereas if your objects have a diameter of 80 pixels, you would need to use larger annotated images e.g. Also make sure that not too many of the annotated objects are touching the border (it’s fine if some do, but it should not be the majority). However, those crops must be big enough to contain entire fully visible objects and provide some context around them. Which size should the training images be? ¶Īs mentioned earlier, it is generally better to annotate a variety of image crops as your training data. Although this is currently not possible, we might add this feature in a future version. Ideally, StarDist could additionally classify all objects while segmenting them. This can either be done manually or with a different classification model. In a second step, you would have to filter out all objects of those types you are not interested in. Alternatively, you can annotate all objects in the training data, such that StarDist will learn to segment objects of all types. While this can work, it might make it more difficult for StarDist to reliably distinguish between objects and background, especially if the visual differences between object types are subtle. First, you can annotate only the object type(s) of interest in your training data, implicitly telling StarDist to consider everything else as background. If there are multiple object/cell types in your image and you only want to segment some of them, you have several options. With multiple nucleus types, is it possible to only segment some or classify in addition to segmentation? ¶
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