Local backup and SQL querying of annotation data¶
Overview:
Annotations represent a significant time investment for the users who generate them and they should be backed up frequently. The simplest way to backup the annotations in a DSA database is to perform a mongodump operation. While frequent mongodump operations are always important to guard against failures they have the following disadvantages: - You need to have access on the server where the annotations are hosted. - The
entire Mongo database is backed up, not just the folder you care about. - You cannot query the database using SQL queries. HistomicsTK has utility functions that allow the recursive backup of a girder database locally as a combination of .json files (most similar to the raw format), tabular files (.csv), and/or an SQLite database.
The SQLite database can easily be viewed using, for example, an offline sqlite viewer or even an online sqlite viewer.
Where to look:
|_histomicstk/
|_annotations_and_masks/
|_annotation_database_parser.py
|_annotation_and_mask_utils.py -> parse_slide_annotations_into_tables()
|_tests/
|_test_annotation_database_parser.py
|_test_annotation_and_mask_utils.py -> test_parse_slide_annotations_into_table()
[1]:
import os
import pandas as pd
import sqlalchemy as db
from histomicstk.utils.girder_convenience_utils import connect_to_api
from histomicstk.annotations_and_masks.annotation_database_parser import (
dump_annotations_locally, parse_annotations_to_local_tables)
Connect and set parameters¶
We use an api key to connect to the remote server, set the girder ID of the folder we want to backup, and set the local path where the backup will be stored.
[2]:
gc = connect_to_api(
apiurl='http://candygram.neurology.emory.edu:8080/api/v1/',
apikey='kri19nTIGOkWH01TbzRqfohaaDWb6kPecRqGmemb')
# This is the girder ID of the folder we would like to backup and parse locally
SAMPLE_FOLDER_ID = '5e24c20dddda5f8398695671'
# This is where the annotations and sqlite database will be dumped locally
savepath = '/home/mtageld/Desktop/tmp/concordance/'
Examine functions for pulling annotation data¶
This is the main function you will be using to walk the folder and pull the annotations from the remote server
[3]:
print(dump_annotations_locally.__doc__)
Dump annotations of folder and subfolders locally recursively.
This reproduces this tiered structure locally and (possibly) dumps
annotations there. Adapted from Lee A.D. Cooper
Parameters
-----------
gc : girder_client.GirderClient
authenticated girder client instance
folderid : str
girder id of source (base) folder
local : str
local path to dump annotations
save_json : bool
whether to dump annotations as json file
save_sqlite : bool
whether to save the backup into an sqlite database
dbcon : sqlalchemy.create_engine.connect() object
IGNORE THIS PARAMETER!! This is used internally.
callback : function
function to call that CAN accept AT LEAST the following params
- item: girder response with item information
- annotations: loaded annotations
- local: local directory
- monitorPrefix: string
- dbcon: sqlalchemy.create_engine.connect() object
You can just add kwargs at the end of your callback definition
for simplicity.
callback_kwargs : dict
kwargs to pass along to callback. DO NOT pass any of the parameters
item, annotations, local, monitorPrefix, or dbcon as these will be
internally passed. Just include any specific paremeters for the
callback. See parse_annotations_to_local_tables() above for
an example of a callback and the unir test of this function.
This optionally calls the following function to parse annotations into tables that are added to an sqlite database.
[4]:
print(parse_annotations_to_local_tables.__doc__)
Parse loaded annotations for slide into tables.
Parameters
----------
item : dict
girder response with item information
annotations : dict
loaded annotations
local : str
local directory
save_csv : bool
whether to use histomicstk.annotations_and_masks.annotation_and_mask.
parse_slide_annotations_into_tables() to get a tabular representation
(including some simple calculations like bounding box) and save
the output as two csv files, one representing the annotation documents
and the other representing the actual annotation elements (polygons).
save_sqlite : bool
whether to save the backup into an sqlite database
dbcon : sqlalchemy.create_engine.connect() object
IGNORE THIS PARAMETER!! This is used internally.
monitorPrefix : str
text to prepend to printed statements
Case 1: Simple backup¶
The simplest case is to backup the information about the girder folders, items, and annotations as .json files, with a folder structure replicated locally as it is in the girder database. The user may also elect to save the folder and item/slide information (but not the annotations) as the following tables in a SQLite database:
folders: all girder folders contained within the folder that the user wants to backup. This includes an ‘absolute girder path’ convenience column. The column ‘_id’ is the unique girder ID.
items: all items (slide). The column ‘_id’ is the unique girder ID, and is linked to the folders table by the ‘folderId’ column.
Here is the syntax:¶
[5]:
# recursively save annotations -- JSONs + sqlite for folders/items
dump_annotations_locally(
gc, folderid=SAMPLE_FOLDER_ID, local=savepath,
save_json=True, save_sqlite=True)
: save folder info
Participant_1: save folder info
Participant_1: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): save item info
Participant_1: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): load annotations
Participant_1: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): save annotations
Participant_1: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): save item info
Participant_1: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): load annotations
Participant_1: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): save annotations
Participant_1: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): save item info
Participant_1: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): load annotations
Participant_1: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): save annotations
Participant_1: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): save item info
Participant_1: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): load annotations
Participant_1: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): save annotations
Participant_1: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): save item info
Participant_1: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): load annotations
Participant_1: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): save annotations
Participant_2: save folder info
Participant_2: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): save item info
Participant_2: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): load annotations
Participant_2: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): save annotations
Participant_2: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): save item info
Participant_2: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): load annotations
Participant_2: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): save annotations
Participant_2: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): save item info
Participant_2: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): load annotations
Participant_2: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): save annotations
Participant_2: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): save item info
Participant_2: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): load annotations
Participant_2: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): save annotations
Participant_2: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): save item info
Participant_2: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): load annotations
Participant_2: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): save annotations
Check the results¶
[6]:
!tree '/home/mtageld/Desktop/tmp/concordance/'
/home/mtageld/Desktop/tmp/concordance/
├── Concordance.json
├── Concordance.sqlite
├── Participant_1
│ ├── Participant_1.json
│ ├── TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs_annotations.json
│ ├── TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs.json
│ ├── TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs_annotations.json
│ ├── TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs.json
│ ├── TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs_annotations.json
│ ├── TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs.json
│ ├── TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs_annotations.json
│ ├── TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs.json
│ ├── TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs_annotations.json
│ └── TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs.json
└── Participant_2
├── Participant_2.json
├── TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs_annotations.json
├── TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs.json
├── TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs_annotations.json
├── TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs.json
├── TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs_annotations.json
├── TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs.json
├── TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs_annotations.json
├── TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs.json
├── TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs_annotations.json
└── TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs.json
2 directories, 24 files
Query the database¶
[7]:
# Connect to the database
sql_engine = db.create_engine(
'sqlite:///%s/Concordance.sqlite' % savepath)
dbcon = sql_engine.connect()
[8]:
# folders table
folders_df = pd.read_sql_query(
"""
SELECT "_id", "name", "folder_path"
FROM "folders"
;""", dbcon)
folders_df
[8]:
| _id | name | folder_path | |
|---|---|---|---|
| 0 | 5e24c20dddda5f8398695671 | Concordance | UncrossPolygonTest/Concordance/ |
| 1 | 5e24c0dfddda5f839869556c | Participant_1 | UncrossPolygonTest/Concordance/Participant_1/ |
| 2 | 5e24c0d3ddda5f8398694f06 | Participant_2 | UncrossPolygonTest/Concordance/Participant_2/ |
[9]:
# items table
items_df = pd.read_sql_query(
"""
SELECT "_id", "name", "folderid"
FROM "items"
;""", dbcon)
items_df
[9]:
| _id | name | folderId | |
|---|---|---|---|
| 0 | 5e24c0dfddda5f8398695571 | TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD... | 5e24c0dfddda5f839869556c |
| 1 | 5e24c0dfddda5f8398695586 | TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B... | 5e24c0dfddda5f839869556c |
| 2 | 5e24c0dfddda5f83986955b1 | TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA2... | 5e24c0dfddda5f839869556c |
| 3 | 5e24c0dfddda5f83986955c1 | TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E4... | 5e24c0dfddda5f839869556c |
| 4 | 5e24c0e0ddda5f83986955d8 | TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F7... | 5e24c0dfddda5f839869556c |
| 5 | 5e24c0dbddda5f839869531a | TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD... | 5e24c0d3ddda5f8398694f06 |
| 6 | 5e24c0dbddda5f8398695342 | TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B... | 5e24c0d3ddda5f8398694f06 |
| 7 | 5e24c0dbddda5f8398695372 | TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA2... | 5e24c0d3ddda5f8398694f06 |
| 8 | 5e24c0dcddda5f8398695387 | TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E4... | 5e24c0d3ddda5f8398694f06 |
| 9 | 5e24c0dcddda5f83986953aa | TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F7... | 5e24c0d3ddda5f8398694f06 |
[10]:
# cleanup
import shutil
shutil.rmtree(os.path.join(savepath))
os.mkdir(savepath)
Case 2: Parse annotations to tables¶
Besides everything outlined above, we could also parse the annotations into tables in the SQLite database and not just save the raw JSON files. This is a little slower because loops through each annotation element. Beside the tables above, the following extra tables are saved into the SQLite database:
annotation_docs: Information about all the annotation documents (one document is a collection of elements like polygons, rectangles etc). The column ‘annotation_girder_id’ is the unique girder ID, and is linked to the ‘items’ table by the ‘itemid’ column.
annotation_elements: Information about the annotation elements (polygons, rectangles, points, etc). The column ‘element_girder_id’ is the unique girder ID, and is linked to the ‘annotation_docs’ table by the ‘annotation_girder_id’ column.
Here’s the syntax:¶
[11]:
# recursively save annotations -- parse sqlite
dump_annotations_locally(
gc, folderid=SAMPLE_FOLDER_ID, local=savepath,
save_json=False, save_sqlite=True,
callback=parse_annotations_to_local_tables,
callback_kwargs={
'save_csv': False,
'save_sqlite': True,
},
)
Participant_1: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): load annotations
Participant_1: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): run callback
Participant_1: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): parse to tables
Participant_1: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): load annotations
Participant_1: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): run callback
Participant_1: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): parse to tables
Participant_1: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): load annotations
Participant_1: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): run callback
Participant_1: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): parse to tables
Participant_1: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): load annotations
Participant_1: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): run callback
Participant_1: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): parse to tables
Participant_1: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): load annotations
Participant_1: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): run callback
Participant_1: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): parse to tables
Participant_2: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): load annotations
Participant_2: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): run callback
Participant_2: slide 1 of 5 (TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD7-A61535786297.svs): parse to tables
Participant_2: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): load annotations
Participant_2: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): run callback
Participant_2: slide 2 of 5 (TCGA-A2-A0YM-01Z-00-DX1.A48B4C96-2CC5-464C-98B7-F0F92AE56533.svs): parse to tables
Participant_2: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): load annotations
Participant_2: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): run callback
Participant_2: slide 3 of 5 (TCGA-A7-A0DA-01Z-00-DX1.5F087009-16E9-4A07-BA24-62340E108B17.svs): parse to tables
Participant_2: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): load annotations
Participant_2: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): run callback
Participant_2: slide 4 of 5 (TCGA-AR-A1AY-01Z-00-DX1.6AC0BE3B-FFC5-4EDA-9E40-B18CAAC52B81.svs): parse to tables
Participant_2: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): load annotations
Participant_2: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): run callback
Participant_2: slide 5 of 5 (TCGA-BH-A0BG-01Z-00-DX1.0838FB7F-8C85-4687-9F70-D136A1063383.svs): parse to tables
Check the result¶
[12]:
!tree '/home/mtageld/Desktop/tmp/concordance/'
/home/mtageld/Desktop/tmp/concordance/
├── Concordance.sqlite
├── Participant_1
└── Participant_2
2 directories, 1 file
Query the database¶
[13]:
# Connect to the database
sql_engine = db.create_engine(
'sqlite:///%s/Concordance.sqlite' % savepath)
dbcon = sql_engine.connect()
[14]:
# annotation documents
docs_df = pd.read_sql_query(
"""
SELECT "annotation_girder_id", "itemId", "item_name", "element_count"
FROM 'annotation_docs'
;""", dbcon)
docs_df.head()
[14]:
| annotation_girder_id | itemId | item_name | element_count | |
|---|---|---|---|---|
| 0 | 5e24c0dfddda5f8398695573 | 5e24c0dfddda5f8398695571 | TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD... | 1 |
| 1 | 5e24c0dfddda5f8398695575 | 5e24c0dfddda5f8398695571 | TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD... | 4 |
| 2 | 5e24c0dfddda5f839869557a | 5e24c0dfddda5f8398695571 | TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD... | 5 |
| 3 | 5e24c0dfddda5f8398695580 | 5e24c0dfddda5f8398695571 | TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD... | 1 |
| 4 | 5e24c0dfddda5f8398695582 | 5e24c0dfddda5f8398695571 | TCGA-A1-A0SK-01Z-00-DX1.A44D70FA-4D96-43F4-9DD... | 1 |
[15]:
# annotation elements
elements_summary = pd.read_sql_query(
"""
SELECT "group", count(*)
FROM 'annotation_elements'
GROUP BY "group"
;""", dbcon)
elements_summary
[15]:
| group | count(*) | |
|---|---|---|
| 0 | Necrosis_or_Debris | 6 |
| 1 | Mostly_Blood | 3 |
| 2 | Mostly_Tumor | 10 |
| 3 | Arteriole_or_Veinule | 6 |
| 4 | Evaluation | 10 |
| 5 | Exclude | 20 |
| 6 | Exclude | 23 |
| 7 | Mostly_Blood | 3 |
| 8 | Mostly_Fat | 9 |
| 9 | Mostly_Lymph | 2 |
| 10 | Mostly_Lymphocytic_Infiltrate | 36 |
| 11 | Mostly_PlasmaCells | 9 |
| 12 | Mostly_Tumor | 83 |
| 13 | Necrosis_or_Debris | 10 |
Sample screenshots¶
