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MoNuSeg grand-challenge organized at MICCAI 2018

Please cite the following papers if you use this repository-

Kumar, N., Verma R. et al., "A Multi-organ Nucleus Segmentation Challenge," in IEEE Transactions on Medical Imaging 2019

Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., & Sethi, A. (2017). A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE transactions on medical imaging, 36(7), 1550-1560

File name Description
Aggregated_Jaccard_Index_v1_0 Compute average AJI across all nuclei for each image
compute_AJI Compute average AJI (across all nuclei) per image for each participant
Ensemble_mask Combine instance masks of top 5 techniques to get ensemble mask using majority voting
correct_nd_missing_nuc_count Count correctly classified nuclei and missing nuclei in each predicted mask of top 5 techniques
he_to_binary_mask_final Use H&E stained image along with associated xml file to generate binary and colored mask
Nuclei-Segmentation An implementation of Mask R-CNN algorithm for nuclei segmentation

Nuclei-Segmentation

An implementation of Mask R-CNN algorithm using Matterport library for nuclei segmentation from whole slide images of tissue sections can be found from the links given below:

Testing code

Trained weights

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This repository contains my implementations of the algorithms which we used for evaluation of the MoNuSeg challenge at MICCAI 2018.

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