View
0
Download
0
Category
Preview:
Citation preview
The problem State of the art Proposed approach Achieved results
Combined Radiology and Pathology Classification of BrainTumors
Rozpoznanie guza mózgu na podstawie obrazu radiologicznego i patologicznego
Piotr Giedziun
Supervisor:dr hab. inz. Henryk Maciejewski
4 March 2016
Piotr Giedziun Wrocław University of Technology 4 March 2016 1 / 25
The problem State of the art Proposed approach Achieved results
Outline
1 The problem
2 State of the art
3 Proposed approach
4 Achieved results
Piotr Giedziun Wrocław University of Technology 4 March 2016 2 / 25
The problem State of the art Proposed approach Achieved results
Definitions
Cancer
Cancer occurs when abnormal cells grow out of control
Brain tumor
Benign or Malignant
Over time, a low-grade tumor can become a high-grade tumor
Brain tumors are classified as grade I, grade II, or grade III, or grade IV
Piotr Giedziun Wrocław University of Technology 4 March 2016 3 / 25
The problem State of the art Proposed approach Achieved results
Brain tumor - Survival rate (5 years or more)
FIGURE – Based on data from SEER 18 2005-2011, cancer.gov
Piotr Giedziun Wrocław University of Technology 4 March 2016 4 / 25
The problem State of the art Proposed approach Achieved results
Brain tumor - Survival by stage
FIGURE – Ovarian cancer, Five-year stage-specific relative survival rates, adults (ages 15-99), Anglia Cancer Net-work, 1987-2008
Piotr Giedziun Wrocław University of Technology 4 March 2016 5 / 25
The problem State of the art Proposed approach Achieved results
Brain tumor - Diagnosis process
General Practitioner
Neurologist
MR or CT - Radiologists
(tumor confirmed)
Neurosurgeon
Benign
Pathologist
(final diagnosis)
Malignant
2 Pathologists
(final diagnosis)
(clear image)
Piotr Giedziun Wrocław University of Technology 4 March 2016 6 / 25
The problem State of the art Proposed approach Achieved results
Diagnosis problems
Problems
Diverse shapes, sizes andappearances of tumors
Relies on histopathologic examination(biopsy examination)
Waiting for tests and to start treatment
Radiology imaging is used only toestablish location, size and whether itis benign and malignant tumor
Targets in the UK
No more than 2 months wait between thedate the hospital receives an urgent GPreferral for suspected cancer and startingtreatment
FIGURE – Glioblastoma cells
FIGURE – Oligodendroglioma cells
Piotr Giedziun Wrocław University of Technology 4 March 2016 7 / 25
The problem State of the art Proposed approach Achieved results
Diagnosis problems
Problems
Diverse shapes, sizes andappearances of tumors
Relies on histopathologic examination(biopsy examination)
Waiting for tests and to start treatment
Radiology imaging is used only toestablish location, size and whether itis benign and malignant tumor
Targets in the UK
No more than 2 months wait between thedate the hospital receives an urgent GPreferral for suspected cancer and startingtreatment
FIGURE – Glioblastoma cells
FIGURE – Oligodendroglioma cells
Piotr Giedziun Wrocław University of Technology 4 March 2016 7 / 25
The problem State of the art Proposed approach Achieved results
Aims & Limitations
Aims
Research & build a segmentation mechanism for the MRI scans (ROI selection)
Research & build a classifier based on the segmented radiological images
(if possible) Combine the Pathology-based classification with radiology-basedclassifier
Limitations
Limited access to the MRI samples with the diadnosis provided by the doctor
Conservative environment - only non-black box models
Piotr Giedziun Wrocław University of Technology 4 March 2016 8 / 25
The problem State of the art Proposed approach Achieved results
Related work
Brain tumor segmentation
The topic of brain segmentation is relatively popular thanks to BraTS challengeSeveral supervised and unsupervised algorithms were proposed
Random Decision Forest that classifies voxelsFuzzy C-means clusteringMean Shift and K-means clustering
Brain tumor classification
Slightly less popular subject (current diagnosis fully rely on histopathologyimaging)Feature extraction
Extraction of structure informationFeature selection
GLCM (Gray-Level Co-occurrence Matrix)
Piotr Giedziun Wrocław University of Technology 4 March 2016 9 / 25
The problem State of the art Proposed approach Achieved results
Influential articles
Joana Festa and Sérgio Pereira and José António Mariz and Nuno Sousa andCarlos A. SilvaAutomatic Brain Tumor Segmentation of Multi-sequence MR images usingRandom Decision ForestsProceedings of NCI-MICCAI BRATS 2013, Nagoya, Japan, 2013
Nitish Zulpe and Vrushsen PawarGLCM Textural Features for Brain TumorInternational Journal of Computer Science, 2012
Hassan Khotanlou, Olivier Colliot, and Isabelle BlochAutomatic brain tumor segmentation using symmetry analysis and deformablemodelsNationale Superieure des Telecommunications, 2007
Piotr Giedziun Wrocław University of Technology 4 March 2016 10 / 25
The problem State of the art Proposed approach Achieved results
Brain tumor - Modified diagnosis process
General Practitioner
Neurologist
MR or CT - Radiologists & piece of software
(tumor confirmed & partial diagnosis)
Neurosurgeon
Benign
Pathologist
(final diagnosis)
Malignant
2 Pathologists
(final diagnosis)
(clear image)
Piotr Giedziun Wrocław University of Technology 4 March 2016 11 / 25
The problem State of the art Proposed approach Achieved results
Data set
0 5 10 15 20 25 30 35
Sample No.
100
200
300
400
500
600
700
800
900
Avera
ge inte
nsi
ty
0 5 10 15 20 25 30 35
Sample No.
0
1000
2000
3000
4000
5000
6000
7000
Max inte
nsi
ty
0 5 10 15 20 25 30 35
Sample No.
200
250
300
350
400
450
500
550
Wid
th
FIGURE – Plots of different attributes of the data set
Angle 1 Angle 2 Angle 3
FIGURE – Viewing angles of MRI scan
Piotr Giedziun Wrocław University of Technology 4 March 2016 12 / 25
The problem State of the art Proposed approach Achieved results
Data set
Summary
27 cases with lower grade glioma tumors
13 of them with Oligodendroglioma and 14 with Astrocytoma
Each case has 3 or 4 MRI scans (T1, T1C, FLAIR, and T2)
Provided samples were taken using different hardware
0 5 10 15 20 25 30 35
Sample No.
100
200
300
400
500
600
700
800
900
Avera
ge inte
nsi
ty
0 5 10 15 20 25 30 35
Sample No.
0
1000
2000
3000
4000
5000
6000
7000
Max inte
nsi
ty
0 5 10 15 20 25 30 35
Sample No.
200
250
300
350
400
450
500
550
Wid
th
FIGURE – Plots of different attributes of the data set
Angle 1 Angle 2 Angle 3
FIGURE – Viewing angles of MRI scan
Piotr Giedziun Wrocław University of Technology 4 March 2016 13 / 25
The problem State of the art Proposed approach Achieved results
Pre-processing
0 50 100 150 200 250
Intensity level
0
1000
2000
3000
4000
5000
Num
er
of
pix
els
HistogramTresholded binary map Extracted skull binary map
FIGURE – Process of skull extraction
FLAIR skull figure T2 skull figure
FIGURE – Skulls properties in FLAIR and T2
Piotr Giedziun Wrocław University of Technology 4 March 2016 14 / 25
The problem State of the art Proposed approach Achieved results
Pre-processing
0 20 40 60 80 100 120 140 160 180
Intensity level
0
1000
2000
3000
4000
5000
6000
Num
er
of
pix
els
Histogram before FLAIR before
0 20 40 60 80 100 120 140 160 180
Intensity level
010002000300040005000600070008000
Num
er
of
pix
els
Histogram after FLAIR after
FIGURE – Median filter effect on image histogram
Piotr Giedziun Wrocław University of Technology 4 March 2016 15 / 25
The problem State of the art Proposed approach Achieved results
Segmentation - K-Means
0.1 0.0 0.2 0.4 0.6 0.8 1.0
The silhouette coefficient values
Clu
ster
label
0
1
2
3
The silhouette plot for the various clusters.
Feature space for the X
0.00.2
0.40.6
0.81.0
1.2 Feature space
for th
e Y
0.00.2
0.40.6
0.81.0
1.2 Featu
re s
pace
for
the Inte
nsi
ty
2024
6
8
10
12
14
The visualization of the clustered data.
FIGURE – Silhouette analysis for K-Means(k=5)
Piotr Giedziun Wrocław University of Technology 4 March 2016 16 / 25
The problem State of the art Proposed approach Achieved results
Segmentation - Combined
K-Means segmentation Symetry analysis segmentation Result
K-Means segmentation Symetry analysis segmentation Result
K-Means segmentation Symetry analysis segmentation Result
FIGURE – Segmentation with results
Piotr Giedziun Wrocław University of Technology 4 March 2016 17 / 25
The problem State of the art Proposed approach Achieved results
Segmentation - Alternatives
3 4 5 6 7 8
Number of clusters
0
20
40
60
80
100
120
Sco
res
Modified K-Means vs K-Means
Mini Batch K-Means
K-Means
FIGURE – Mini K-Means
3 4 5 6 7 8
Number of clusters
0
20
40
60
80
100
120
Sco
res
Agglomerative Clustering vs K-Means
Agglomerative
K-Means
FIGURE – Agglomerative cluste-ring
3 4 5 6 7 8
Number of clusters
0
20
40
60
80
100
120
Sco
res
Modified K-Means vs K-Means
Modified K-Means
K-Means
FIGURE – K-Means with position
Piotr Giedziun Wrocław University of Technology 4 March 2016 18 / 25
The problem State of the art Proposed approach Achieved results
Classification
Tested methods
Feature extraction & evaluation
Texture features extraction with Gray-Level Co-Occurrence Matrix
Texture features extraction with Local Binary Pattern
Classification algorithms
SVM (Support vector machine)
Gaussian Naive Bayes
Logistic Regression
Random Forest
Piotr Giedziun Wrocław University of Technology 4 March 2016 19 / 25
The problem State of the art Proposed approach Achieved results
Classification - Feature extraction & evaluation
Selected features (out of 59)
Tumor volume (in mm3)
Tumor position (x,y,z) calculated fromthe middle of the brain
Metrics intensity of tumor area
8 bins of intensity histogram
0 2000 4000 6000 8000 10000 12000 14000 16000
Values of FLAIR 4th histogram bin
0
5000
10000
15000
20000
Valu
es
of
FLA
IR 5
th h
isto
gra
m b
in
Oligodendroglioma
Astrocytoma
FIGURE – Selected features extracted from data set
pos_x
0.60.4
0.20.0
0.20.4
0.6
pos_y
0.6
0.4
0.2
0.00.2
0.40.6
pos_
z
0.4
0.3
0.2
0.1
0.0
0.1
0.2
0.3
0.4
The visualization of tumor position. Temporal lobe
Frontal lobe
Brain
Oligodendroglioma
Astrocytoma
FIGURE – Tumor positional features
Piotr Giedziun Wrocław University of Technology 4 March 2016 20 / 25
The problem State of the art Proposed approach Achieved results
Classification - Texture features extraction with GLCM & LBP
Oligodendroglioma Astrocytoma
O #1 O #2 O #3 O #4 O #5
A #1 A #2 A #3 A #4 A #5
0 10 20 30 40 50 60
GLCM Dissimilarity
0.2
0.0
0.2
0.4
0.6
0.8
1.0
GLV
M C
orr
ela
tion
1
23
4
51
2
3
45 O
A
FIGURE – Co-occurence matrix features for Oligodendroglioma and Astrocytoma
Piotr Giedziun Wrocław University of Technology 4 March 2016 21 / 25
The problem State of the art Proposed approach Achieved results
Radiology imaging
Tumor segmentationMETHOD BEST SCOREMini Batch K-Means (5 clusters) 89.027% (std : 5.408)K-Means (5 clusters) 88.168% (std : 5.264)K-Means with position (5 clusters) 86.026% (std : 5.282)Agglomerative Clustering 88.956% (std : 10.632)
Cancer classificationMETHOD BEST SCORERandom Forest Classifier 87.000% (std : 12.991)Logistic Regression 81.297% (std : 5.744)Logistic Regression (texture) 68.285% (std : 0.082)
Piotr Giedziun Wrocław University of Technology 4 March 2016 22 / 25
The problem State of the art Proposed approach Achieved results
Combined Radiology and Pathology
0.0 0.2 0.4 0.6 0.8 1.0
Radiology Classifier estimate
0.0
0.2
0.4
0.6
0.8
1.0
Path
olo
gy C
lass
ifie
r est
imate
Oligodendroglioma
Astrocytoma
FIGURE – Comparison of Pathology and Radiology results (average estimations of Oligodendroglioma cancer foreach sample)
Piotr Giedziun Wrocław University of Technology 4 March 2016 23 / 25
The problem State of the art Proposed approach Achieved results
Results
Conclusion
Random Forest classifier validated with k-fold cross validation had averageaccuracy of 87.0%
Pre-processing of the input data is a hand-crafted process, that had to beperformed
K-Means had the best score out of Mini Batch K-Means, K-Means with modifiedinput vector (with position), and Agglomerative clustering
Piotr Giedziun Wrocław University of Technology 4 March 2016 24 / 25
The problem State of the art Proposed approach Achieved results
Combined Radiology and Pathology Classification of BrainTumors
Rozpoznanie guza mózgu na podstawie obrazu radiologicznego i patologicznego
Piotr Giedziun
Supervisor:dr hab. inz. Henryk Maciejewski
4 March 2016
Piotr Giedziun Wrocław University of Technology 4 March 2016 25 / 25
Recommended