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 #