Kharagpur Learning, Imaging and Visualization (KLIV)

22 Pages·2017·10.43 MB·Indonesian

Kharagpur Learning, Imaging and Visualization
(KLIV)
Research Group
Department of Electrical Engineering
Indian Institute of Technology Kharagpur, India
2017
Sponsors and Collaborators
Group Leader
Debdoot Sheet, PhD
Assistant Professor, Department of Electrical Engineering
Indian Institute of Technology Kharagpur
EMAIL: [email protected]
WEB: www.facweb.iitkgp.ernet.in/∼debdoot/
PHONE: [phone]
We currently focus on understanding artificial in-
telligence and developing deep neural networks
for visual computing including medical image anal-
ysis and surgical informatics.
Dr. Sheet’s research interest include computational medical imaging, ma-
chine learning, image and multidimensional signal processing, surgical an-
alytics and informatics, visualization and augmented reality technology de-
sign. He received the Ph. D. degree from the Indian Institute of Technol-
ogy Kharagpur in 2014 for studies in ultrasonic and optical imaging and
machine learning for developing in situ histopathology. He is a former
DAAD fellow and was a visiting scholar at the Technical University of
Munich during 2011-12. He is a member of IEEE, ACM, IUPRAI, SPIE,
and BMESI and serves as an Editor of IEEE Pulse since 2014.
Research Group Members
Abhijit Guha Roy Kausik Das Niladri Garai Debarghya China
Rachana Sathish Aupendu Kar Ram Nagar Akshit Mantri
Srichandra Chi- Francis Tom Abhinav Agarwal Aarushi Agrawal
lappagari
Research Group Members
Oindrila Saha Shaswat Datta Abhishek Pal Shubh Agrawal
Neha Banerjee Nikhil Naik Himanshu Chaud- Prashant Yadav
hary
Abhishek Mohap- Inderpreet Kaur
atra
Generative Models for Simulating Ultrasound
Ultrasound imaging makes use of backscattering of waves during their in-
teraction with scatterers present in biological tissues. Simulation of syn-
theticultrasoundimagesisachallengingproblemonaccountofinabilityto
completely model various factors of which some include intra-/inter scan-
line interference, transducer to surface coupling, artifacts on transducer el-
ements, inhomogeneous shadowing and nonlinear attenuation. While var-
ious approaches to ultrasound simulation has been developed, approaches
that produce patho-realistic images typically solve wave space equations
making it computationally expensive and slow to operate. We propose
a generative adversarial network (GAN) inspired approach for fast simu-
lation of patho-realistic ultrasound images. We apply the framework to
intravascular ultrasound (IVUS) simulation. We demonstrate that the net-
work generates realistic appearing images evaluated with a visual Turing
test indicating an equivocal confusion in discriminating simulated from
real. We also quantify the shift in tissue specific intensity distributions of
the real and simulated images to prove their similarity.
TomF,SheetD,“SimulatingPatho-realisticUltrasoundImagesusingDeep
Generative Networks with Adversarial Learning”, Proc. IEEE Int. Symp.
on Biomedical Imaging (ISBI), 2018. [https://arxiv.org/abs/1712.
07881]
Semantic Segmentation
Optical coherence tomography (OCT) is used for non-invasive diagnosis
of diabetic macular edema assessing the retinal layers. In this paper, we
propose a new fully convolutional deep architecture, termed ReLayNet,
for end-to-end segmentation of retinal layers and fluid masses in eye OCT
scans. ReLayNetusesacontractingpathofconvolutionalblocks(encoders)
to learn a hierarchy of contextual features, followed by an expansive path
of convolutional blocks (decoders) for semantic segmentation. ReLayNet
is trained to optimize a joint loss function comprising of weighted logistic
regression and Dice overlap loss. The framework is validated on a publicly
available benchmark dataset with comparisons against five state-of-the-art
segmentation methods including two deep learning based approaches to
substantiate its effectiveness.
Guha Roy A, Conjeti S, Karri SPK, Sheet D, Katouzian A, Wachinger C,
Navab N, “ReLayNet: retinal layer and fluid segmentation of macular op-
tical coherence tomography using fully convolutional networks”, Biomedi-
cal Optics Express, vol. 8, no. 8., pp. 3627-3642, 2017.
Domain Adaptation
Domain adaptation deals with adapting behaviour of machine learning
based systems trained using samples in source domain to their deployment
in target domain where the statistics of samples in both domains are dis-
similar. The task of directly training or adapting a learner in the target do-
main is challenged by lack of abundant labeled samples. In this paper we
propose a technique for domain adaptation in stacked autoencoder (SAE)
based deep neural networks (DNN) performed in two stages: (i) unsuper-
vised weight adaptation using systematic dropouts in mini-batch training,
(ii) supervised fine-tuning with limited number of labeled samples in target
domain. We experimentally evaluate performance in the problem of retinal
vessel segmentation where the SAE-DNN is trained using large number of
la- beled samples in the source domain (DRIVE dataset) and adapted using
less number of labeled samples in target domain (STARE dataset).
Guha Roy A, Sheet D, “DASA: Domain Adaptation in Stacked Autoen-
coders using Systematic Dropout”, Proc. 3rd Asian Conference on Pattern
Recognition (ACPR), pp. 735-739, 2015.
Conjeti S, Katouzian A, Guha Roy A, Peter L, Sheet D, Carlier S, Laine
A, Navab N, “Supervised domain adaptation of decision forests: Transfer
of models trained in vitro for in vivo intravascular ultrasound tissue char-
acterization”, Medical Image Analysis, vol. 32, no. 1, pp. 1-17, 2016.
Digital Pathology
Histopathology forms the gold standard for confirmed diagnosis of a suspi-
cious hyperplasia being benign or malignant and for its sub-typing. While
techniques like whole-slide imaging have enabled computer assisted anal-
ysis for exhaustive reporting of the tissue section, it has also given rise
to the big-data deluge and the time complexity associated with processing
GBs of image data acquired over multiple magnifications. We propose a
deep convolutional neural network (CNN) based solution, where we anal-
yseimagesfromrandomnumberofregionsofthetissuesectionatmultiple
magnifications without any necessity of view correspondence across mag-
nifications.
Das K, Conjeti S, Guha Roy A, Chatterjee J, Sheet D, “Multiple Instance
Learning of Deep Convolutional Neural Networks for Breast Histopathol-
ogy Whole Slide Classification”, Proc. IEEE Int. Symp. on Biomedical
Imaging (ISBI), 2018.
DasK,KarriSPK,GuhaRoyA,ChatterjeeJ,SheetD,“ClassifyingHistopathol-
ogy Whole-Slides using Fusion of Decisions from Deep Convolutional
Network on a Collection of Random Multi-views at Multi-magnifications”,
Proc. IEEE Int. Symp. on Biomedical Imaging (ISBI), pp. 1024-1027,
2017.
Graphical Models and Random Walks
Intravascular ultrasound (IVUS) is commonly used to adjunct imaging
based diagnosis of vascular plaques. Since speckle intensity in IVUS im-
ages are inherently stochastic in nature, clinicians critically face the chal-
lenge of identifying vessel boundaries. In this chapter we present a method
for segmenting the lumen and external elastic laminae in IVUS images of
coronary arteries via. a two stage framework. The first stage employs
understanding of physics of ultrasound-tissue interaction as a statistical
mechanical parametric framework and ensemble learning of this paramet-
ric space to initialize lumen and external elastic luminae boundary contour.
Subsequently in the second stage these initialized contours are solved us-
ing a random walker solver. While both the stages are employed while
segmenting individual IVUS frames, in case of complete pullbacks, the
two stages are employed only on the first frame and only the second stage
is employed on subsequent neighbouring frames using initialization propa-
gated as belief from the previously segmented frame.
China D, Mitra P, Sheet D, “Segmentation of Lumen and External Elastic
Laminae in Intravascular Ultrasound Images using Ultrasonic Backscatter-
ing Physics Initialized Multiscale Random Walks”, Proc. MedImage Work-
shop - 10th Indian Conf. Comp. Vis., Graph., Image Process (ICVGIP),
2016.