336 - Nuclei segmentation and analysis using Detectron2 & YOLOv8
This video tutorial is an entire project spanning from data download to training object detection models to analysis and plotting. It covers the following key tasks, with downloadable code for every task:
Downloading data from Kaggle
Cleaning up the data
Converting masks to coco json and YOLOv8 annotations
Visualizing annotations
Training Detectron2 (Mask R-CNN) for object detection
Training YOLOv8 for object detection
Code is available here: https://github.com/bnsreenu/python_fo...
Dataset downloaded from: https://www.kaggle.com/datasets/ipate...
Dataset description: https://arxiv.org/abs/2308.01760
Summary of the dataset:
The NuInsSeg dataset contains more than 30k manually segmented nuclei from 31 human and mouse organs and 665 image patches extracted from H&E-stained whole slide images. We also provide ambiguous area masks for the entire dataset to show in which areas manual semantic/instance segmentation were impossible.
Human organs:
cerebellum, cerebrum (brain), colon (rectum), epiglottis, jejunum, kidney, liver, lung, melanoma, muscle, oesophagus, palatine tonsil, pancreas, peritoneum, placenta, salivary gland, spleen, stomach (cardia), stomach (pylorus), testis, tongue, umbilical cord, and urinary bladder
Mouse organs:
cerebellum, cerebrum, colon, epiglottis, lung, melanoma, muscle, peritoneum, stomach (cardia), stomach (pylorus), testis, umbilical cord, and urinary bladder)
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