- Видео 15
- Просмотров 1 995
Brian Northan
Добавлен 15 дек 2019
Pixel based Machine Learning in Napari
This video shows how to optimize pixel based machine learning on SEM grain images and images of cells with irregular borders using Napari. The examples are based on a popular Napari Tutorial from Kyle Harrington. We add a new live view of the labeling that is generated from the pixel based segmentation. This additional feedback is useful because it shows the user how well the model is separating individual objects.
Просмотров: 84
Видео
Jupyter Notebook 3D Projections
Просмотров 7521 день назад
This video shows how to render 3D images in a Jupyter Notebook using slice views and 3D projections from the tnia-python library. We also show several interesting interactive projections.
Challenging Cellpose part 2
Просмотров 732 месяца назад
Part 2 of challenging Cellpose Image.sc question. More info on augmentation and training parameters and a closer look at the flows and probability maps.
Challenging Cellpose part1
Просмотров 1092 месяца назад
A challenging Cellpose problem from the Image.sc forum. In these images there are larger cells with very long protrusions and we have to tweak Cellpose training and evaluation parameters to try and optimize segmentation.
Bees part 3, Sparse Stardist + Mobile Sam (Yolo+SAM)
Просмотров 1074 месяца назад
This video, though slightly rough around the edges, aims to demonstrate the performance of two deep learning approaches on bee data. The first approach is MobileSam, a combination of YOLO and SAM, and the second is Stardist with sparse labels. Starting from version 0.9, Stardist no longer requires complete rectangular ROIs for labeling; you can leave portions of the label empty. This feature ca...
Semantic Segmentation with PyTorch and Napari
Просмотров 1194 месяца назад
This video shows how to use Napari (with a customized labeling widget) and PyTorch to train a deep learning UNET semantic segmentation model network to detect roots. A similar workflow could be used for image with vessels, cracks or other filament like structure. We use augmentation to supplement a very small number of labels.
Stardist connect 2D in 3D
Просмотров 2115 месяцев назад
This video shows how to use Stardist 2D to create a training set for Stardist 3D. We first create a simulated 3D image, then add blur, noise and random mis-alignment. Then we apply a stardist2D-connectin3D strategy to generate 3D labels. We train Stardist with these imperfect labels and show that Stardist3d does not overfit the imperfections.
Exploring Stardist scale and shape
Просмотров 626 месяцев назад
A video showing how to create simulated images to test the performance of a custom Stardist model on objects at varying scale and shape (with some objects being very elongated).
Napari/Stardist bee segmentation the sequel.
Просмотров 1847 месяцев назад
In this video I show how to retrain a model after we get new data (in this case a new bee image). I talk about augmentation, test-prediction, validation-prediction, and self-prediction. Throughout the video I use Napari as both a viewer and a labeling tool.
segment everything demo
Просмотров 3448 месяцев назад
Quick demo showing how to use the napari-segment-everything plugin. This is a plugin that can be used to explore the large, overlapping label collection that is returned from the Meta AI SAM segmentation model. The plugin can be installed from the Napari hub.
napari sam labeling
Просмотров 1749 месяцев назад
Short video demonstrating how to label an image in Napari with a combination of SAM and Napari labelling tools.
insect egg labelling
Просмотров 3811 месяцев назад
Shows how to quickly correct labels with Napari, then retrain a stardist model, in the case where another method is about 95% accurate but needs a few fixes.
bee labelling for imagesc
Просмотров 216Год назад
This video was made to help with this imagesc image forum question forum.image.sc/t/how-to-count-bees-pattern-recognition-and-segmentation/90115/8 It shows how to label, train a stardist network for segmentation then relabel the bee image using ipython notebooks and the napari viewer. Code for the example is here github.com/True-North-Intelligent-Algorithms/tnia-python/tree/main/notebooks/image...