Date: Tue, 19 Mar 2024 00:01:55 -0500 (CDT)
Message-ID: <404203739.27417.1710824515116@tcia-wiki-rh-1.ad.uams.edu>
Subject: Exported From Confluence
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Summary
Detection, segmentation and classification of nuclei are fundament=
al analysis operations in digital pathology. Existing state-of-the-art appr=
oaches demand extensive amounts of supervised training data from pathologis=
ts and may still perform poorly in images from unseen tissue types. We prop=
ose an unsupervised approach for histopathology image segmentation that syn=
thesizes heterogeneous sets of training image patches, of every tissue type=
. Although our synthetic patches are not always of high quality, we ha=
rness the motley crew of generated samples through a generally applicable i=
mportance sampling method.
This proposed approach, for the first time, re-weighs the tra=
ining loss over synthetic data so that the ideal (unbiased) generalization =
loss over the true data distribution is minimized. This enables us&nbs=
p;to use a random polygon generator to synthesize approximate cellular stru=
ctures (i.e., nuclear masks) for which no real examples are given in many t=
issue types, and hence, GAN-based methods are not suited. In addition,=
we propose a hybrid synthesis pipeline that utilizes textures in real hist=
opathology patches and GAN models, to tackle heterogeneity in tissue textur=
es. Compared with existing state-of-the-art supervised models, our ap=
proach generalizes significantly better on cancer types without training da=
ta. Even in cancer types with training data, our approach achieves the=
same performance without supervision cost.
In this dataset we release code and nucleus segmentations in =
whole slide tissue images with quality control results for over 5000 Whole =
Slide Images (WSI) in The Cancer Genome Atlas (TCGA) repository. =
There are two subsets of data: (1) automatic nucleus segmentat=
ion data of 5,060 whole slide tissue images of 10 cancer types, with qualit=
y control results, and (2) manual nucleus segmentation data of 1,356 image =
patches from the same 10 cancer types plus additional 4 cancer types.
These 5,060 Whole =
Slide Images (WSIs) are from the following 10 cancer types:
BLCA Bladder urothelial carcinoma BRCA Breast inva=
sive carcinoma CESC<=
/span> Cervical squamous cell carcinoma and endocervical aden=
ocarcinoma GBM Glioblastoma Multiforme LUAD Lung adenocarcinoma LUSC Lun=
g squamous cell carcinoma PAAD Pancreatic adenocarcinoma PRAD Prostate ad=
enocarcinoma SKCM Skin Cutaneo=
us Melanom a <=
span style=3D"color: rgb(0,0,0);">UCEC Uterine Corpus =
Endometrial Carcinom a
Note that you can also download segmentation data of following 4 c=
ancer types, although they are not officially verified or released. <=
/p>
COAD Colon adenocarcinoma READ Rectal adenocarcinoma STAD Stomach adenocarcinoma UVM Uveal Me=
lanoma
Data Access
Data Access
Data Type
Download
License
Tissue Slide Segmentation Results (SVS, 665 GB)<=
/td>
Download (Download and apply the IBM-Aspe=
ra-Connect plugin to your br=
owser to retrieve this faspex package)
List of histopathology slides (TXT, 348.5 KB )
Whole slide image-level quality control results =
(TXT, 151.4 KB)
Segmentation region checking results (TXT, 169.4=
KB)
Readme (DOCX, 20kb)
crosswalk between patch filenames and TCGA=
case identifiers (TXT, 72 kb)
Click the Versions tab for more in=
fo about data releases.
Please contact help=
@cancerimagingarchive.net with any questions regarding usage.
Collections Used in th=
is Third Party Analysis
Below is a list of the Collections used in these =
analyses:
TCGA-BLCA ,
TCGA-BRCA ,
TCGA-CESC ,
TCGA-COAD ,
TCGA-GBM ,
TCGA-LUAD ,
TCGA-LUSC ,
TCGA-PAAD,
TCGA-PRAD ,
TCGA-READ ,
TCGA-SKCM,
TCGA-STAD ,
TCGA-UCEC ,
TCGA-UVM
Additional Resources for this Dataset
Additional information about
Additional visual segmentation data can be found on =
PathDB
Manual nucleus segmentation data of 1,365 patches: These 1,365 patches =
are randomly extracted from all 14 cancer types mentioned above. This data =
contains original H&E stained histopathology image patches, and instanc=
e-level segmentation masks. the process is in the readme.docx file and=
a crosswalk between patch filenames and TCGA case identifiers are withi=
n Pan-Cancer-Nuclei-Seg_1365patches_to_TCGA-ID_readme.=
txt file.
Detailed Description
Detailed Description
Additional visual segmentation data can be =
found on PathDB
Manual nucleus segmentation data of 1,365 patches
These 1,365 patches are randomly extracted from all 14 cancer type=
s mentioned above. This data contains original H&E stained histopatholo=
gy image patches, and instance-level segmentation masks. Additional informa=
tion about the process is in the readme.docx file and a crosswalk between p=
atch filenames and TCGA case identifiers are within Pan-Can=
cer-Nuclei-Seg_1365patches_to_TCGA-ID_readme.txt file.
Citations & Data Usage Policy
Versions
Version 1 (Curren=
t): 2020/02/08
Data Type
Download all or Query/Filter
Tissue Slide Images (SVS, 1,200 GB=
)
List of histopathology slides (TXT, 348.5 KB )
WSI quality control results (TXT, 151.4 KB)
Segmentation region checking results (TXT, 169.4=
KB)
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