Cancer tissue evaluation

1. HUMAN BREAST CANCER
TISSUE EVALUATION USING
FLUORESCENCE
MICROSCOPY

2. AIM
To image human breast cancer tissues using 3D fluorescence microscopy and to automate
the diagnosis and classification of different grades of breast cancer tissues using image
processing techniques.
PRIMARY OBJECTIVES
• To obtain human breast cancer tissue samples
• To image tissue samples in both 2D and 3D fluorescence microscopes
• To segment cancer cell and its nuclei
• To check if the tissues are benign or malignant from the extracted features
• To train the neural network to classify the different grades of breast cancer from both 2D
and 3D fluorescence microscopy images automatically
SECONDARY OBJECTIVES
• To compare the accuracy of classification between 2D and 3D fluorescence microscopy
images
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3. BACKGROUND AND JUSTIFICATION:
• Automation of breast cancer detection plays a vital role in improving the healthcare provided
to the rural parts of India. It saves time and also human error is avoided.
• Early detection and diagnosis of breast cancer can prevent and decrease the mortality rate
among women in rural parts of India.
• Obtaining 3D images of biopsy from patients are more accurate due to the volume and
depth information it conveys.
• Sometimes during sample preparation and tissue sectioning the nucleus and chromatin of
cell gets sliced and may give wrong information which leads to error during automation of
cancer detection.
• It is easier to find sliced nucleus from 3D images which may not be visible in 2D images and
hence increases the accuracy of automated detection.
HYPOTHESIS
Three dimensional fluorescence microscopy images gives much more accuracy in training the
classifier and can give extra information such as depth and volume from which new
diagnosing parameters may be found which can be helpful for prognosis and early detection
of cancer.
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4. INTRODUCTION:
• Cancer is the result of mutations or
abnormal changes in genes which are
responsible for controlling the growth of
cells.
• One of the confirmatory tests for cancer
diagnosis is done by taking biopsy from
patients and viewing it under the
microscope and the shape and structure of
cells and its nuclei are studied and also the
cell distribution in tissues are observed.
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Fig 1: Breast tumour
cells imaged under
fluorescence
microscopy stained
with pan-cytokeratin
antibody
Fig 2: Corresponding
cell nuclei stained
with DAPI (4,6-
diamidino-2-
phenylindole) imaged
under fluorescence
microscopy
Picture courtesy:
https://www.ucsf.edu/news/2007/01/3786/wittmann?utm_source=reddit_
scienceshill&utm_medium=reddit&utm_campaign=2007_hidden_universe

5. • Fluorescence microscopy is an imaging
modality that allows the observation of
cellular components like DNA, proteins
and other organelles by specific
labelling with fluorophores.
• It can be used in cancer investigations
by studying about the nuclei content in
cancer cells.
• Cancer cells tend to have denser
chromatin content in the area of
tumour. Based on the difference in
position of the HES 5 gene normal
breast tissues can be differentiated
from cancerous breast tissues.
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Nuclei are in green,
purple is the Golgi
apparatus and blue
is actin
Fig 3: Fluorophore
labelled breast
cancer cells imaged
under fluorescence
microscopy
Picture courtesy:
https://www.ucsf.edu/news/2007/01/3786/wittmann?utm_source=reddit_
scienceshill&utm_medium=reddit&utm_campaign=2007_hidden_universe

6. 01-02-2024 Department of Biomedical Engineering 6
Sample
Preparation for
both cell and
cell nuclei
Imaging of
samples
Pre-processing
Segmentation of
cell and cell
nuclei
Post-processing
Classification of
cancer grades
Comparison of
accuracy of
classifier with
both 2D and 3D
images
Feature
extraction
Selection of
significant
features
BLOCK DIAGRAM

7. METHODOLOGY:
• STUDY DESIGN: Case control study
• SAMPLE SIZE:
n = Sample size; p = Expected prevalence; d = Precision required
p = 40% = 0.40 d = 5% = 0.05 Z(1-α/2) = 1.96
n = Z2
(1-α/2) p (1-p) / d2
n = (1.96)2 0.40 (1 - 0.40) / (0.05)2
n = 368.79 ≈ 370 samples.
• STATISTICAL ANALYSIS
Descriptive statics (Mean, standard deviation, and variance) will be included to compare
quantitative data. Chi-square test will be used.
The results will be expressed in 95% confidence and value of p<0.05 will be considered to
be statistically significant. SPSS version 16 software is used for data analysis
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8. • NO. OF GROUPS: Three groups-Normal, benign and malignant each having (n=123) samples
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Tissue samples
(n=370)
Benign
(Non-cancerous)
(n=123)
Malignant
(Cancerous)
(n=123)
Grade 1
(n=40)
Grade 2
(n=40)
Grade 3
(n=40)
Normal
(n=123)
n= No. of samples

9. • INCLUSION CRITERIA
• All patients who are undergoing core needle and surgical biopsy of breast tumour.
• EXCLUSION CRITERIA
• Patients who are undergoing fine needle aspiration (FNA)
• Patients who are not interested in this research and who are not giving consent
• INTERVENTION: Not applicable. Our study focusses only on diagnosis
• CONTROL: Compared with normal tissue samples
• DOSAGES OF DRUGS & FREQUENCY WITH DURATION: No drugs will be
administered to the patients during the course of study
• INVESTIGATIONS/PROCEDURES TO BE DONE ETC.: Only breast tissue samples
will be obtained.
• TYPE OF RANDOMIZATION & METHOD USED: Simple randomization is followed.
Subjects will be taken, as they come, normal or overweight.
• METHOD OF ALLOCATION CONCEALMENT: Not significant for the study.
• BLINDING/MASKING IF ANY: Not significant for the study.
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10. 01-02-2024 Department of Biomedical Engineering 10
Thick and thin
tissue sections
Cell nuclei Cell
2D
image
3D
image
2D
image
3D
image
Segmentation
Significant Feature
extraction
Classification
DAPI
Pan cyto-keratin
antibody
PROCEDURE

11. • SETTING IN WHICH SUBJECTS WILL BE RECRUITED FROM: All patients who have to
undergo surgical and core needle breast tissue biopsy from May 2019- December 2020
PERIOD OF RECRUITMENT: May 2019 - December 2020
• POTENTIAL RISKS INVOLVED TO THE PARTICIPANTS OF THIS STUDY: There are no
known risks associated with this research.
• POTENTIAL BENEFITS: This research will help in automating biopsy diagnosis which will
assist the pathologist in quicker diagnosis.
• DO YOU NEED EXEMPTION FROM OBTAINING INFORMED CONSENT FROM STUDY
SUBJECTS: No not required.
• WHETHER CONSENT FORMS PART 1 AND PART 2 IN ENGLISH AND IN LOCAL
LANGUAGE ARE ENCLOSED? Yes
• IS THERE A CHILDREN’S ASSENT? No. It is not required for this study
• HAS CRF BEEN ENCLOSED? No, not applicable for this study
• DOCUMENTS ATTACHED FOR REGULATORY CLINICAL TRIALS: No, clinical trials is not
performed in this study
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12. REFERENCES
[1] Chichen Fu et al, “Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation”- arXiv (2018), 1801.07198 .
[2] Leonid Kostrykin et al, “Segmentation of cell nuclei using intensity-based model fitting and sequential convex programming”- IEEE 15th International Symposium on
Biomedical Imaging (ISBI) (2018) 978-1-5386-3636-7/18.
[3] Rhea Chitalia et al, “Algorithms for differentiating between images of heterogeneous tissue across fluorescence microscopes ”- Biomedical optics express (2016) Vol.
7, No. 9.
[4] Jun Kong et al, “Automated cell segmentation with 3D fluorescence microscopy images”- IEEE (2015) 978-1-4799-2374-8/15.
[5] Ndeke Nyirenda, Daniel L. Farkas, and V. Krishnan Ramanujan, “Preclinical evaluation of nuclear morphometry and tissue topology for breast carcinoma detection
and margin assessment”- Springer Science+Business Media (2011) 126(2): 345–354.
[6] Jenna L. Mueller et al, “Quantitative Segmentation of Fluorescence Microscopy Images of Heterogeneous Tissue: Application to the Detection of Residual Disease in
Tumor Margins”- PLOS ONE(2013) Volume 8, Issue 6.
[7] Kaustav Nandy et al, “Automatic Segmentation and Supervised Learning Based Selection of Nuclei in Cancer Tissue Images”- Cytometry Part A (2012) 81A:743–754.
[8] Alexandre Dufour et al, “Segmenting and Tracking Fluorescent Cells in Dynamic 3-D Microscopy With Coupled Active Surfaces ”- IEEE Transactions on image
processing (2005), Vol. 14, No. 9.
[9] Jeroen A.M. Belie¨n et al, “Confocal DNA Cytometry: A Contour-Based Segmentation Algorithm for Automated Three-Dimensional Image Segmentation”- Cytometry
(2002) 49:12–21.
[10] Gang Lin et al, “A Multi-Model Approach to Simultaneous Segmentation and Classification of Heterogeneous Populations of Cell Nuclei in 3D Confocal Microscope”-
Cytometry Part A (2007) 71A: 724736 .
[11] Umesh Adiga et al, “High-Throughput Analysis of Multispectral Images of Breast Cancer Tissue”- IEEE Transactions on image processing (2006), Vol. 15, No. 8.
[12] Umesh Adiga et al, “Characterization and automatic counting of F.I.S.H. signals in 3-D tissue images”-Image Anal Stereol(2001) 20:41-52.
[13] Shekar singh et al, “Breast cancer detection and classification of histopathological images”-International journal of engineering science and technology (2011) Vol. 3,
No. 5.
[14] Munezza Ata Khan et al, “Detection and Characterization of Antinuclear Antibody using fluorescence image processing”- IEEE International Conference on Robotics
and Emerging Allied Technologies in engineering (2014) 978-1-4799-5132-1/14
[15] Michael J. Sanderson et al, “Fluorescence Microscopy”-Cold Spring Harb Protoc. (2016) (10)
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