The histo-pathological analysis of tissue sections is the gold standard to assess the presence of many complex diseases, such as tumors and it is expected to be at the center of the AI revolution in medicine, prevision supported by the increasing success of deep learning applications to digital pathology. The aim of histolab is to provide a tool for Whole Slide Images (WSIs) processing in a reproducible environment to support clinical and scientific research. histolab is designed to handle WSIs, automatically detect the tissue, and retrieve informative tiles.

1. : an Open Source Python Library
for Reproducible Digital Pathology
9-10-11 Novembre, 2021
Ernesto Arbitrio
ernesto.arbitrio@gmail.com
@__pamaron__
Alessia Marcolini
alessia.marcolini@hk3lab.ai
@viperale

2. 2
Alessia Ernesto
Data Science M.Sc. @ TU Eindhoven / TU Berlin
Junior Data Scientist @ HK3lab
PyCon Italia Organizer
Senior Backend Engineer @ YouGov PLC
PyCon Italia Organizer
Open Source Contributor

3. 3
I am so excited to work
on this new digital
pathology project!!
day 1
me!

4. Histopathology
Primary diagnostic resource for
the identification of complex
diseases, in particular of tumors
https://www.poliambulanza.it/dipartimenti/dipartimento-di-oncologia/anatomia-patologica
Section of Pathology and Tumour Biology, University of Leeds
Digital Pathology
Scanning of histopathological glass
slides to create Whole Slide Images
4

5. 5
I am so excited to work
on this new digital
pathology project!!
Let’s find some
literature first!
day 1

6. 6
day 3
*Reading papers
without any mention
of repo containing the
source code*

7. 7
day 5
Yay, finally some code!

8. 8
day 5
*opens code*

9. 9
day 5
*opens code*

10. 10
“Tagliatelle” effect, 2017
https://twitter.com/viperale/status/1202980338693226496
day 30

11. Whole Slide Images
Multi-resolution image (e.g. 5× and 20×)
Pyramidal format
Up to 90,000px × 30,000px
Very large in size up to 10GB
Scanner vendor specific file format
e.g. .svs, .vms, .ndpi, .tif, .bif, .scn
Ad-hoc software
for viewing and processing
Artifacts like shadows,
mold, pen marks
Image from Y. Wang et al. 2012.
SurfaceSlide: A multitouch digital pathology platform
10.1371/journal.pone.0030783
11

12. Whole Slide Images
Multi-resolution image (e.g. 5× and 20×)
Pyramidal format
Up to 90,000px × 30,000px
Very large in size up to 10GB
Scanner vendor specific file format
e.g. .svs, .vms, .ndpi, .tif, .bif, .scn
Ad-hoc software
for viewing and processing
Artifacts like shadows,
mold, pen marks
Image from Y. Wang et al. 2012.
SurfaceSlide: A multitouch digital pathology platform
10.1371/journal.pone.0030783
11
https://twitter.com/tdmckee/status/1456585006982340611?s=21

13. Whole Slide Images
Multi-resolution image (e.g. 5× and 20×)
Pyramidal format
Up to 90,000px × 30,000px
Very large in size up to 10GB
Scanner vendor specific file format
e.g. .svs, .vms, .ndpi, .tif, .bif, .scn
Ad-hoc software
for viewing and processing
Artifacts like shadows,
mold, pen marks
Image from Y. Wang et al. 2012.
SurfaceSlide: A multitouch digital pathology platform
10.1371/journal.pone.0030783
11

14. why?
Using WSIs directly as input to DL is unfeasible
Preprocessing to create smaller subwindows ("tiles") is required
Preprocessing steps usually poorly detailed in research papers
Leading to results that are hard to reproduce
Need for a reference high quality preprocessing software
To enable faster prototyping and faster experimentation
12

15. why?
Using WSIs directly as input to DL is unfeasible
Preprocessing to create smaller subwindows ("tiles") is required
Preprocessing steps usually poorly detailed in research papers
Leading to results that are hard to reproduce
Need for a reference high quality preprocessing software
To enable faster prototyping and faster experimentation
12

16. why?
Using WSIs directly as input to DL is unfeasible
Preprocessing to create smaller subwindows ("tiles") is required
Preprocessing steps usually poorly detailed in research papers
Leading to results that are hard to reproduce
Need for a reference high quality preprocessing software
To enable faster prototyping and faster experimentation
12
https://paperswithcode.com/

17. why?
Using WSIs directly as input to DL is unfeasible
Preprocessing to create smaller subwindows ("tiles") is required
Preprocessing steps usually poorly detailed in research papers
Leading to results that are hard to reproduce
Need for a reference high quality preprocessing software
To enable faster prototyping and faster experimentation
12
https://www.paperswithoutcode.com/
https://paperswithcode.com/

18. why?
Using WSIs directly as input to DL is unfeasible
Preprocessing to create smaller subwindows ("tiles") is required
Preprocessing steps usually poorly detailed in research papers
Leading to results that are hard to reproduce
Need for a reference high quality preprocessing software
To enable faster prototyping and faster experimentation
12

19. why?
Using WSIs directly as input to DL is unfeasible
Preprocessing to create smaller subwindows ("tiles") is required
Preprocessing steps usually poorly detailed in research papers
Leading to results that are hard to reproduce
Need for a reference high quality preprocessing software
To enable faster prototyping and faster experimentation
12
https://twitter.com/alexkyllo/status/1457072262520004632

20. why?
Using WSIs directly as input to DL is unfeasible
Preprocessing to create smaller subwindows ("tiles") is required
Preprocessing steps usually poorly detailed in research papers
Leading to results that are hard to reproduce
Need for a reference high quality preprocessing software
To enable faster prototyping and faster experimentation
12
https://twitter.com/alexkyllo/status/1457072262520004632
https://twitter.com/petebankhead/status/1407630531290927105?s=21

21. 13
team
me!
Nicole
Ernesto
day 60

22. new open source Python package for
reproducible Whole Slide Images preprocessing
aimed at an easy
integration with a Deep Learning pipeline
14

23. unifying community-validated procedures
for slide preprocessing and tiles extraction
introducing best practices from software
engineering: automated testing, code versioning
and code reviews, Continuous Integration
on top of state-of-the-art and well-known libraries,
e.g. OpenSlide, NumPy and scikit-image
approach
15

24. Histolab features
Interoperability
between different
formats
up to 9 supported
formats from the
major scanner
vendors
#1
16

25. Histolab features
Interoperability
between different
formats
up to 9 supported
formats from the
major scanner
vendors
#1
Automatic tissue
detection and
segmentation
by using a fixed
sequence of image
filters
#2
16

26. Histolab features
Interoperability
between different
formats
up to 9 supported
formats from the
major scanner
vendors
#1
Automatic tissue
detection and
segmentation
by using a fixed
sequence of image
filters
#2
Automatic
informative tiles
retrieval
cropped regions
from tissue areas
found in #2
#3
16

27. Histolab features
Interoperability
between different
formats
up to 9 supported
formats from the
major scanner
vendors
#1
Automatic tissue
detection and
segmentation
by using a fixed
sequence of image
filters
#2
Automatic
informative tiles
retrieval
cropped regions
from tissue areas
found in #2
#3
Easy access to
sample data
from TCGA and
OpenSlide
save to the system
cache and import
them
#4
16

28. Histolab in action
WSI Image dataset (tiles) DL pipeline
Prostate Cancer Sample, TCGA-PRAD
16,000px × 15,316px
Magnification 5×
512px × 512px
Magnification 20×
512px × 512px
17

29. Tiles extraction #3
in less than 10 lines of code
>>> from histolab.data import breast_tissue
>>> _, path = breast_tissue()
>>> from histolab.slide import Slide
>>> slide = Slide(path, "path/to/processed")
>>> from histolab.tiler import RandomTiler
>>> random_tiles_extractor = RandomTiler(
tile_size=(512, 512),
n_tiles=10,
level=2,
seed=42,
check_tissue=True,
)
>>> random_tiles_extractor.extract(slide)
1. download breast
tissue sample from
TCGA
2. create a Slide object
3. create a Tiler
4. extract!
18

30. Tissue detection and tiles extraction
Original WSI
Tissue
Detection
Random Tiles
Breast Cancer Sample, TCGA-BRCA
96,972px × 30,682px
Magnification 20×
512px × 512px
19
RandomTiler

31. Tissue detection and tiles extraction
20
ScoreTiler
>>> from histolab.scorer import NucleiScorer
>>> scorer = NucleiScorer()
>>> from histolab.tiler import ScoreTiler
>>> scored_tiles_extractor = ScoreTiler(
scorer,
tile_size=(512, 512),
n_tiles=10,
level=2,
seed=42,
check_tissue=True,
)
>>> scored_tiles_extractor.extract(slide)
Representation of the score assigned to each
extracted tile by the NucleiScorer.
Ovarian Cancer Sample, TCGA-OV
30,001px × 33,987px
Nuclei Mask
512px × 512px tile

32. Original image 1. Grayscale filter 2. Otsu threshold 3. Binary dilation
4. Remove small holes 5. Remove small objects 6. Final mask 7. Biggest Tissue Area Box
Tissue detection #2
by using this fixed sequence of image filters Nobuyuki Otsu 1979.
A threshold selection method from gray-level histograms
10.1109/TSMC.1979.4310076
21

33. >>> from histolab.filters.image_filters import Compose, OtsuThreshold, RgbToGrayscale
>>> from histolab.filters.morphological_filters import (
BinaryDilation,
RemoveSmallHoles,
RemoveSmallObjects,
)
>>> filters = Compose(
[
RgbToGrayscale(),
OtsuThreshold(),
BinaryDilation(),
RemoveSmallHoles(),
RemoveSmallObjects(),
]
)
>>> filters(image)
Original image 1. Grayscale filter 2. Otsu threshold 3. Binary dilation
4. Remove small holes 5. Remove small objects 6. Final mask 7. Biggest Tissue Area Box
Nobuyuki Otsu 1979.
A threshold selection method from gray-level histograms
10.1109/TSMC.1979.4310076
22
Tissue detection #2

34. Remove artifacts
pen markers
Original image 1. Grayscale filter 2. Otsu threshold
3. Apply Mask 4. Green Pen Filter
Pen Filters implementation inspired by
https://github.com/CODAIT/deep-histopath
23

35. 24
Finally some stable
code I can use!
present, day 500
Stable? Is it
tested?

36. 25
present, day 500
Look at this!
100% coverage with
600 unit and
integration tests

37. 25
present, day 500
Look at this!
100% coverage with
600 unit and
integration tests

38. 26
Yes, but how do I get
all of these goodies?
present, day 500

39. GitHub
github.com/histolab/histolab
GitHub Actions
for Continuous Integration

40. GitHub Actions
benchmarks.yml
28
https://histolab.github.io/histolab/dev/bench/

41. GitHub Actions
29
release.yml

42. Docs
histolab.readthedocs.io

43. 31
https://github.com/histolab/histolab/blob/master/CONTRIBUTING.md
Join
Give us a ⭐!

44. $ pip install histolab
Installation
32

45. 33
You will spend more time writing tests than code
but at least you will sleep tight at night
100% coverage doesn’t imply 0% bugs
but stupid mistakes are easily caught
Code formatting and linting is nice
but automatize it to focus on the important stuff
Lessons learned
and notes for future self

46. Histolab is a joint work with
Nicole Bussola, PhD student
@ FBK-MPBA / CIBIO
Thank you
any question?
Alessia Marcolini Ernesto Arbitrio
alessia.marcolini@hk3lab.ai ernesto.arbitrio@gmail.com
@viperale @__pamaron__