Nuclei Segmentation (ilastik)

Description

NEUBIAS-WG5 workflow for nuclei segmentation using ilastik v1.3.2 and Python post-processing.

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Mask-RCNN

Description

This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.

Nuclei Segmentation (Mask-RCNN)

Description

NEUBIAS-WG5 workflow for nuclei segmentation using Mask-RCNN. The workflow uses Matterport Mask-RCNN. Keras implementation. The model was trained with Kaggle 2018 Data Science Bowl images.

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Landmark detection DMBL model prediction

Description

This workflow predict landmark positions on images by using DMBL landmark detection models.

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Landmark detection DMBL model training

Description

This workflow trains DMBL landmark detection models from a dataset of annotated images.

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Landmark detection LC models prediction

Description

This workflow predict landmark positions on images by using LC landmark detection models.

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Landmark detection LC models training

Description

This workflow trains LC landmark detection models from a dataset of annotated images.

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Landmark detection MSET models prediction

Description

This workflow predict landmark positions on images by using MSET landmark detection models.

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Landmark detection MSET models training

Description

This workflow trains MSET landmark detection models from a dataset of annotated images.

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Pixel classification for GlaS challenge with UNet

Description

This workflow segments glands from H&E stained histopathological images
from the Gland Segmentation Challenge (GlaS2015) using deep learning (UNet).
UNet implementation largely inspired from PyTorch-UNet by Milesial. 

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