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README.txt
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Colorectal Nuclear Segmentation and Phenotypes (CoNSeP) Dataset
----------------------------------------------------------------------------------------------------
Overview:
This dataset was first used in our paper named,
"HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images"
If using any part of this dataset or the code associated, you must give appropriate citation to our paper, published in Medical Image Analysis.
----------------------------------------------------------------------------------------------------
Dataset Description:
Each ground truth file is stored as a .mat file, with the keys:
'inst_map'
'type_map'
'inst_type'
'inst_centroid'
'inst_map' is a 1000x1000 array containing a unique integer for each individual nucleus. i.e the map ranges from 0 to N, where 0 is the background and N is the number of nuclei.
'type_map' is a 1000x1000 array where each pixel value denotes the class of that pixel. The map ranges from 0 to 7, where 7 is the total number of classes in CoNSeP.
'inst_type' is a Nx1 array, indicating the type of each instance (in order of inst_map ID)
'inst_centroid' is a Nx2 array, giving the x and y coordinates of the centroids of each instance (in order of inst map ID).
Note, 'inst_type' and 'inst_centroid' are only used while computing the classification statistics.
The values within the class map indicate the category of each nucleus.
Class values: 1 = other
2 = inflammatory
3 = healthy epithelial
4 = dysplastic/malignant epithelial
5 = fibroblast
6 = muscle
7 = endothelial
In our paper we combine classes 3 & 4 into the epithelial class and 5,6 & 7
into the spindle-shaped class.
Total number of nuclei = 24,319
Note, as of 14/03/2020, we switched the GT from .npy files to .mat files for
consistency with the Github repo
------------------------------------------------------------------------------------------
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.