Computer Assisted Grading Schema for Follicular Lymphoma Based on Level Set Formulation

Bharti Arora, Student andSupratik Banerjee

Abstract- In this research paper we are focusing on development

of computer-assisted scheme for Follicular Lymphoma (FL) for

improvising grading scheme.Since Centro-blast enumeration

needs to be performed in malignant follicles, the development of

an automated system to accurately identifyfollicles on digital

images of lymphoid tissue is an important step. In this paper we

describe a novel approach to identify follicles from H&E stained

tissue sections with formulating a local energy, and later

globalize it with neighborhood method for segmenting entire

tissue section and classified them accordingly with artificial

intelligence.We have outperformed the previous iterative

watershed segmentation and classification approaches.

Key Terms- color texture analysis, computer-aided diagnosis,

Follicular lymphoma, grading scheme, H&E stained image.

I. INTRODUCTION

Follicular Lymphoma (FL) is the second most common non­Hodgkins lymphoma in the world with a highly variable clinical course based on the American Cancer Society's statistics[I].Follicular lymphoma (FL) is characterized by an abnormal proliferation of B-lymphocytes (a type of white blood cell) which are carried in the bloodstream or lymph channels to the other parts of the body.

In the current situation diagnosis of Follicular Lymphoma (FL) is carried out manually by visual assessment of tissue samples. The manual assessment may vary from pathologists to pathologists based on their perspective of analyzing the things. So this requires careful and accurate analysis of tissue samples. On the other hand, FL patients with aggressive disease should receive appropriate therapy as soon as possible to increase their chance of remission and to prolongtheir lives [1], [3].The samples of this fatal disease are included in our research and are tested.

Bharti Arora is with Department of Computer Science and Engineering,

Lovely Professional University, lalandhar-144402,India

(email:bharti.2908@gmail.com)

Supratik Banerjee is with Department of Computer Science and

Engineering, Lovely Professional University, Jalandhar-144402,lndia

(email:supratik.15524@lpu.co.in)

978-1-4673-5630-5//13/$3l.00 ©20 l3 IEEE

These important clinical decisions are currently guided by histopathological grading of the tumor. Traditionally the diagnosis of FL is characterized by morphologic, cytogenetic, and immunophenotypic findings in lymph node/tissuebiopsy samples. Now World Health Organization (WHO) has recommended a histopathological grading characterized by count of large malignant cells called Centro-blasts (CB) in ten representative microscopic regions called high power fields (HPF) [1], [5].

Currently pathologists manually count large malignant cells called Centro-blasts (CB) in ten representative microscopic regions called high power fields (HPF) (a standard HPF is defined as 0.159mm2 under 40X objective and 18mm field of view 1) of hematoxilin and eosin (H&E) stained tissue section(s) under microscope and classify the biopsy into one of the three following histological grades according to the average Centro-blast count per HPF [5].

TABLE I WHO CLASSIFICATION OF FOLLICULAR LYMPHOMAS

Grade CBIHPF Risk Category

I 0-5 low risk

II 6-15 low risk

III/A and IIIIB >15 high risk

Moreover, in the current scenario for practical reasons, pathologists typically count CBs that may be possible consequences of over or under grading of FL as well includes inappropriate timing and type of therapy with serious clinical consequences for patients. This qualitative analysis of manual grading affects the accuracy of diagnosis and success of the treatment further.

Therefore we are focusing on to provide a classification approach based on local intensityapproximation i.e. we are focusing on formulating a local criteria to define intensity locally because previously deployed regions based segmentation algorithms [6], [13], [14], [15] are grounded on pixel intensities; therefore quite challenging while segmenting inhomogeneous overlapped imaginary. We strong believe that in real world images intensity varies drastically as we move on to trace new CB, so considerable challenge in image segmentation no matter how we choose descriptor to guide the motion of active contour.

In this paper, we proposed a novel region-based method for image segmentation. From a generally accepted model of images with intensity inhomogeneity, we derive a local intensity criteria, assuming that intensity value have slow varying property locally and therefore define a local function for the intensities in a

neighborhood of each point. This local function is integrated over the neighborhood centers to define an energy functional, which is converted to a level set formulation. Minimization of this energy is achieved by an interleaved process of level set evolution.

This paper is organized as follows. We first review two well-known region-based models for image segmentation in Section II. In Section III, we propose an energy minimization framework for image segmentation and estimation of bias field, which is then converted to a level set formulation in Section IV for energy minimization. Experimental results are given in Section V, followed by a discussion of the relationship between our model and the piecewise smooth Mumford-Shah and piecewise constant Chan-Vese models in Section VI. This paper is summarized in Section VII.

II. DATA COLLECTION & AREA OF STUDY

For this study we are collecting the images by visiting various pathologies in India and have a database of 110 images of Follicular Lymphoma. Image set consist all three graded images of Follicular Lymphoma. We consulted with 3 different pathologists to examine each slide and ask them to extract demonstrative region and appropriate classification (grades) from each slide of the tissue samples yielding a total of 110 images. 27 of the samples were identified as grade I, 34 were identified as grade II and 49 were identified as grade III according, by different pathologist.

III. METHODOLOGY

A. Image Segmentation Let .0 c be the image domain, andJ: .0 � be agiven

medical colored image. In Mumford and Shah formulated the image segmentation problem as follows: given an imageI, find a contour which segments the image into nonoverlapping regions. They proposed the following energy functional[ 16];

:FMS(u, C) = J (u - 1)2 + J1. f IVul2dx + vlCI (1) .n .n IC

First term, in left side is data term which forces u to remain close to 1, middle term is smoothing term, which forces u to remain smooth in the region separated by contour C and third right term is for regularizing the contour C. I C I denotes the length of contour.

Chan and Vese proposed an active contour approach to the Mumford-Shah problem for aspecial case where the image in the functional (1) is a piecewise constant function. For an image on the image domain, they propose to minimize the following energy: [6]

:FCV(<jJ,CVc2) = J lI(x) -clI2H(<jJ(x))dx .n1

+ J lI(x) - c212(1 - H(<jJ(x)))dx .n2

+u J VH(<jJ(x))dx (2) .n

Where,.o1 = .ooutside and .02 = .oinside denote the region outside and inside the contour C respectively.c1 &c2are constants that approximate the image intensity outside and inside the contour C respectively. First two terms, in left side are data terms and third term is the smoothing term.

This model is not suitable for segmenting the images with intensity inhomogeneity as it is difficult to set descriptor value as the intensity in neighborhood varies drastically. In our work we deal with the images of Follicular Lymphoma that have drastically varying intensity because of which sometimesbiased region (foreground) and cytoplasmic( background) is incorrectly classified due to overlaps between the ranges of the intensities in the regions to be segmented. [11]

To overcome the problem confronted while segmenting the images with intensity inhomogeneity, we employ locally defined clustering energyfunction that calculates the local energy of biased region rather than calculating global energy of the entire image [11].This method outlinesa kernel function that clusters the pixels with similar properties (descriptor), hence separates the inner region from the outer region. For a given point XiE .oiwe state the followinglocally defined clustering energy.

fit 2 2

(C,C1(X),C2(X)) = "J(I(X)-Ci (X)) (3) Ex ft * K(x - y)dy

Given a center point x, the locally defined clustering energy can be minimized when the contour C is exactly on the object boundary and c1 and c2approximate the image intensity outside and inside the contour C respectively. To obtain entire object boundary the contour C must be uncovered that minimizes the energy

fit

E overall center points x in the image domain.o. This can be

x achieved by minimizing the integral of over all the center pointsxin the image domain.o.

Therefore, we define the following energy functional

Where, second term is thesmoothing term and ICI is the length of the contour.

Till now we are working on discovering local cluster biased region with homogeneous property. Now we are calculatingenergy

for entire regionn(region with inhomogeneity intensities). To obtainthat,we have to integrate eq.4w.r.t Y where yE n. Now we can rewrite eq. 4in the following manner:

[it

Ex C¢,c1(X),C2(x))

= If Ku(x i=l

- Y)I/(y) - Ci(X) IMd2 ((¢(y))dy (5)

It is computationally difficult to minimize energy globally w.r.t number of contours we find out so we applied iterative process (membership function) to perform localized minimization on every boundary of biased region.

This energy is minimizing on the boundary of every nuclei with respect to c1 (x), c2 (x) as follows:

where

In the above eq. 6 the term (-O,,(tP)(A1e1 - A1e1))is derived as the data fitting energy, and, therefore, is referred to asthe data fitting term. This term is responsible for driving the active contour toward area of interest (object boundaries). The second termhas a length shortening or smoothing effect, which is necessary to maintain the regularity of thecontour i.e. the magnitude of this term represent as an arc length of contour. The third termis called a level set regularizationterm, since it serves to maintain the regularity of contour. ,,11 &A2 � o are the fixed parameters.

B. Classification of segmented Image Grading is a good indicator to predict the extent of

disease and used to decide the treatment strategies. Lymphoma grade is divided into three categories Grade I, Grade II and Grade III. And Grade 3 is further subdivided into two categories grade 3A and grade 3B. It is considered that Grades I, II, and III A are indolent and incurable while Grade III B is considered a critical but curable disease. Grade III/A and Grade III/B are sometimes misclassified but it does not appear to be clinically significant [17].As Grade III/A & Grade IIIIB are not relied on quantitative evaluation (means CB/HPF value remains same).

In the subsequent discussion we are talking about grading schema of follicular lymphoma.

In the last eq. 6 we have obtained three key terms,the middle term is responsible to keep track of the arc length. We can rewrite second term of eq.6 as follows:

2nd =

V (...!:... [1 + cos (TrX)] div ( VtP ) ) (7) 2E E IvtP l

This term contains Xi term belongs to specific region niand we have assumed that local intensities is slow varying within the cluster so simple we can replace value of x in terms of c (center) this center belongs to a particular region this iterative process will

we applicable {nd i : 1}.we can able to find out no of centers

with the help of 2ndterm and the magnitude of this term denotes as arc length of contour. This way we are able to confine to special parameter that is fed to neural network for segmented image classification purpose.

We tested supervised machine learning algorithms to determine lymphoma grades in histopathological images using the features defined previously. In supervised learning, the data is first divided into training and test data sets. A classifier is trained with the labeled training data and the classes of the test data are then decided using this classifier. Our classifier consist of only a hidden layer consist of 10 neurons we use 25 images as training dataset and rest 85 images as test dataset from 110 images of data set. In this paper we have displayed the result of 3 segmented images. We trained the neural network with two parameters i.e.the curvature and center of the nuclei, and classified the results according to their respective grades.

We refer three pathologists and ask them for classification of entire database into respective grades (manually). Then we constructed a training dataset (25 images) from their prescribed grading and trained the neural network accordingly. Then the training data set is fed to the neural network and rest of the database (85 images) treated as test data set which is used to calculate how well the current neural network quantifying the performance of the proposed systemi.e. accuracy for Computer­assisted black box with respect to Expected outcome (averaging of grade from different pathologist) and Experiment outcome that is provided by our proposed model.

C. Proposed diagram of computer- assisted system We have divided our proposed diagram into 3 subparts; First,

we segment our input image by local fitting energy technique discussed in section III after that we extract two important feature from segmented image (no of centers & length of curvature). In the next part we fed these parameters to classifier (Neural Network) to grade image appropriately.

Input Image Segmented Image Neural Network

:�-Network trained

with the number of

centers &the length

of curvature

Fig.I. proposed model of Follicular Lymphoma grading system

I. RESUL TS AND DISCUSSION

Classified grade

Grade II

Grade II!

Grade III

Bias corrected image

Bias corrected image

Bias corrected image

Fig. 2. (a), (d), (g) are the original histopathological Imaginary (test data set) grade II, IIU A, IIUB respectively (b), (e), (h) segmented image after 100 iterations and( c), (t), (i) images are the extraction of area of interest from whole imaginary (back ground & foreground)

Image

FL 1

FL 2

FL 3

TABLE II EXPECTED OUTCOME

Patho 1 Patho 2

Grade I Grade I

Grade IIIIA Grade II

Grade II Grade II

Patho 3

Grade II

Grade IIIIB

Grade II

In this study we have consider only those cases in which doctor's subjectivity issue conquered. As in the case of second biopsy (FL _2) doctorsare finding difficulty in differentiating the actual count of CB/HPF ratio and in the case of FL _1 they (patho _3) misclassified the grade also.

Our computer assisted grading schema is tested on 85 images and we summarize the result as follows:

27 grade I

34 grade II

12 grade III/A

37 grade III/B

TABLE III EXPERIMENTED OUTCOME

Grade I Grade II Grade III/A

89 11 0

7 91 2

0 0 67

0 0 22

Grade/B

0

0

37

78

Our proposed system relies on quantification of number of the centers and thelength of curvature (i.e. objective value). The

classification of Grade III into its respective subclass (grade III I A &B) needs visual assessment more than the CB/HPF ratio as for both the grade CB/HPF ratio will remain same only the difference is in percentage of diffuse component,Centcocytes present in Grade IIII A and in Grade IIIIB follicles consist almost entirely of Centro blast. So our proposed system is not able to differentiate them appropriately. More importantly separation of subclasses of Grade III is not clinically significant [I7].However, our proposed Computer assisted Grading Schema have 89% of accuracy.

IV. CONCLUSION

The primary objective of this research work is to develop a computer assisted tool for grading schema. In this paper we segmented the nuclei by employing locally defined clustering method to identifY their shape. The proposed model is able to classifY the grades of Follicular Lymphoma and has revealed the considerable performance over Iterative Watershed grading algorithm. Our system identifies the Grade III accurately, but is not able to classifY its subclasses Grade IIII A and Grade III/Bappropriately however this c1assificationis not clinically significant.

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