Enabling Toxicologic Pathologists Using QuPath, An Open
Source Image Analysis Software SolutionDaniel Rudmann1, Abigail Godbold1, Maureen O’Brien2, Brent Walling1
1 Ashland OH, 2 Frederick MD
4 CONCLUSIONS1
• QuPath was easily downloaded locally on computers, performed well on a variety of laptops and
desktops, and did not require large storage space for analyses when external drives or Cloud servers
were used
• Numerous types of raw image files from different WSI scanners as well as conventional microscope
cameras were dropped quickly into QuPath for analysis
• The region of analysis (ROA) was simple to define using practical annotation tools. The “Polygon”
tool allowed tracing of a ROA. The “Wand” tool enabled faster annotation and supported exclusions
• Default cell detection settings were good for preliminary nuclear detection and cell counts
• For assessment of cell numbers, smoothing, nuclear area, and DAB/hematoxylin thresholds were
useful in targeting cell populations (lymphocytes, hepatocytes, bone marrow cells, thyroid follicular
cells) while excluding other cells or artifact not part of the ROA
• For defining cells in support of cell volume measurements, modifying cell expansion parameters and
applying nuclear distance measurements were helpful
• QuPath is still in an early release version but based on this preliminary work and the clear
engagement of the developer, it appears to have good potential for utility in toxicologic pathology
ABSTRACT
Table 2: IA Solutions- Challenges, Key Parameters, and Probability of
Technical Success (PTS)
Anatomic pathology is often a semi-quantitative science and assessing certain microscopic changes in
preclinical studies can be laborious and prone to diagnostic drift. Computer-based image analysis (IA)
could simplify numerous tasks common for the bench pathologist such as setting thresholds and
establishing grading criteria for various tissue changes. Commercial IA solutions are available, but
licenses are typically expensive and may limit use to a single computer or workstation. Historically, open
source software image analysis solutions lacked the sophistication to tackle analyses using whole slide
images (WSI). Recently, QuPath emerged as an alternative open-source platform for IA and aims to
help improve the speed, objectivity, and reproducibility of digital pathology analysis in WSI
https://qupath.github.io/. We developed algorithms in QuPath for 3 common diagnoses/measurements
recorded in preclinical toxicology studies. QuPath was easily downloaded locally on computers,
performed well on a variety of laptops and desktops, and did not require large storage space for
analyses. Training pathologists required a modest time investment and a job aide was easily tailored
from materials produced by QuPath developers. Numerous types of raw image files from different WSI
scanners as well as conventional microscope cameras were dropped quickly into QuPath for
analysis. The region of analysis (ROA) was simple to define using practical annotation
tools. Algorithms were developed to evaluate the 3 diagnoses and preliminary data suggested they
increased the diagnostic confidence and efficiency for the pathologists. We conclude that QuPath
appears to have good potential as a tool for bench toxicologic pathologists.
C: Positive Cell Detection
RESULTSFigure 1A-D: General Approach and Effort for IA Set Up in QuPath (Approximate Time
for Each Step in Arrow)
< 5 sec < 10 sec
10
sec-
2 m
in
A: Dropping Analysis File B: Annotation
<2 min/file
D: Quality Review by Pathologist and Evaluating
Additional Tissues/Animals
Study Type/Diagnosis Endpoint(s) Potential ROI
Hepatocellular Hypertrophy Cell Size, Nuclear-Nuclear Distance
Speed, grading, and consistency
Thyroid Follicular Cell Hypertrophy
Cell Size, Nuclear-Nuclear Distance
Speed, grading, and consistency
Tissue Cross Reactivity Positive Cell Number (1-3+) Threshold setting, speed, and consistency
Bone Marrow Cellularity Total Cell Number or Cell Number/Area
Speed, grading, and consistency
Delayed Type Hypersensitivity Total Cell Number or Cell Number/Area
Speed, grading, and consistency
Cell Proliferation Positive Cell Number or Positive Cell Number/Area
Threshold setting, speed, and consistency
Study Type/Diagnosis
Challenge(s) Key Tool or Parameter(s) PTS
Hepatocellular Hypertrophy
Cell border, nuclear distance (Figs. 2-3)
Cell expansion, nuclear area, intensity and nuclear distance
Medium
Thyroid Follicular Cell Hypertrophy
Cell border definition(Fig 4)
Wand annotation tool, cell expansion, intensity threshold parameters
Medium
Tissue Cross Reactivity
Thresholds (Fig 5) Intensity threshold parameters(DAB OD)
High
Bone MarrowCellularity
Nucleardifferentiation (Fig 6)
Nuclear area + intensity threshold (hematoxylin OD)
Medium
Delayed Type Hypersensitivity
Accuracy (Fig 7) Nuclear area + intensity threshold (hematoxylin OD)
High
Cell Proliferation
Cell border definition (Fig 7-8)
Intensity threshold (DAB OD),smoothing, nuclear area
High
3
Figure 4: Thyroid Follicular Cell Analysis
Default Settings Increasing Hematoxylin Threshold Increasing Cell Expansion
Follicular Precipitates Follicular Cell Epithelial Gaps Optimized ImageFigure 5: Thresholding Approach- TCR Studies
Negative 1+ 1-2+
Adjusting thresholds to the eye of the Pathologist to establish parameters-applying
same parameters from slide to slide prevented diagnostic drift
Figure 6: Bone Marrow Cellularity- Nuclear Selection
Reduction of minimum nuclear
area by 50% detected RBC islands
Default settings miss some
RBC islands
Figure 8: Cell Proliferation- Ki67 Tonsillar Enumeration
Initial run (A, B) highlighting positive (red) and negative (blue) nuclear detection and
arrows of nuclei that escaped detection. Optimized (C,D) using smoothing parameters,
altered DAB thresholds, and adjusted nuclear size detection
A
B
C
D
Figure 2: Hepatocellular Hypertrophy- Nuclear SelectionSelection of hepatocytes by adjusting cell/nuclear parameters in QuPath
Initial cell/nuclear detection Optimized selection of hepatocytes
Figure 3: Hepatocellular Hypertrophy- Nuclear to
Nuclear Distance Analysis
Minimizing nuclear-nuclear distance biased when crossing large areas/sinusoids (*).
By setting maximum distance threshold, nuclear-nuclear distance better estimated.
Distance threshold
2 METHODS
Table 1: Study Type/Diagnoses for QuPath IA Analysis and Potential
Return on Investment
Figure 7: DTH Studies- Counting Lymphocytes
Effects of adjusting nuclear area and threshold to increase accuracy of
lymphocyte selection (positive cells red) ; pathologists quality review
indicated middle settings were most accurate (24K cell count)
6 different quantitative challenges were evaluated in this pilot (Table 1)
• Hepatocellular hypertrophy- size/volume (rat)
• Thyroid follicular cell hypertrophy- size/volume (rat)
• Tissue cross reactivity (TCR)- positive cell detection (human)
• Bone marrow cellularity- cell number (rat)
• Delayed type hypersensitivity- cell number (monkey)
• Cell proliferation Ki67 immunohistochemistry- positive cell detection (human)
General QuPath methods (done for all challenges)
• Slides were manually cleaned and scanned on a Leica AT or AT2 at 20 or 40x
• QuPath v 0.1.2 was downloaded at https://qupath.github.io/ using “without administrative rights”
• WSS files (svs) for analysis were directly “dragged” into QuPath for analysis (Fig 1A)
• The polygonal or wand tool was used to annotate images (Fig 1B)
• For slides involving detection of DAB chromogen (TCR, Ki67): AnalyzeCell AnalysisPositive Cell
Detection was used followed (Fig 1C)
• AnalyzeCell AnalysisCell Detection was used at default settings for other non-IHC IA
• Quality review was done by a pathologist to verify cell detection was acceptable based on “challenge”
• If quality review failed, detection settings were modified empirically
• Algorithm settings then applied to additional tissues (Fig 1D) or animals
17K 36K24KCell #