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7 Papers

Deep-Learning-based Segmentation of Organs-at-Risk in the Head for MR-assisted Radiation Therapy Planning

Topics: Medical Imaging and Diagnosis; Magnetic Resonance Imaging (MRI); Image Processing Methods; Applications of Machine Learning; Deep Learning in Bioimaging

Authors: László Ruskó 1 , Marta E. Capala 2 , Vanda Czipczer 1 , Bernadett Kolozsvári 1 , Borbála Deák-Karancsi 1 , Renáta Czabány 3 , Bence Gyalai 3 , Tao Tan 1 , Zoltán Végváry 4 , Emőke Borzasi 4 , Zsófia Együd 4 , Renáta Kószó 4 , Viktor Paczona 4 , Emese Fodor 4 , Chad Bobb 5 , Cristina Cozzini 6 , Sandeep Kaushik 6 , Barbara Darázs 3 , Gerda M. Verduijn 2 , Rachel Pearson 7 , Ross Maxwell 7 , Hazel Mccallum 7 , Juan A. Hernandez Tamames 8 , Katalin Hideghéty 4 , Steven F. Petit 2 and Florian Wiesinger 6

Affiliations: 1 GE Healthcare, Budapest, Hungary , 2 Erasmus MC Cancer Institute, Department of Radiation Oncology, Rotterdam, The Netherlands , 3 GE Healthcare, Szeged, Hungary , 4 University of Szeged, Department of Oncotherapy, Szeged, Hungary , 5 GE Healthcare, Milwaukee, U.S.A. , 6 GE Healthcare, Munich, Germany , 7 Newcastle University, Northern Institute for Cancer Research, Newcastle, U.K. , 8 Erasmus MC, Department of Radiology and Nuclear Medicine, Rotterdam, The Netherlands

Digital Object Identifier: 10.5220/0010235000002865

Proceedings of BIOIMAGING 2021/BIOSTEC, ISBN: 978-989-758-490-9

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Segmentation of organs-at-risk (OAR) in MR images has several clinical applications; including radiation therapy (RT) planning. This paper presents a deep-learning-based method to segment 15 structures in the head region. The proposed method first applies 2D U-Net models to each of the three planes (axial, coronal, sagittal) to roughly segment the structure. Then, the results of the 2D models are combined into a fused prediction to localize the 3D bounding box of the structure. Finally, a 3D U-Net is applied to the volume of the bounding box to determine the precise contour of the structure. The model was trained on a public dataset and evaluated on both public and private datasets that contain T2-weighted MR scans of the head-and-neck region. For all cases the contour of each structure was defined by operators trained by expert clinical delineators. The evaluation demonstrated that various structures can be accurately and efficiently localized and segmented using the presented framework. The contours generated by the proposed method were also qualitatively evaluated. The majority (92%) of the segmented OARs was rated as clinically useful for radiation therapy.

BINGR: Binary Search based Gaussian Regression

Topics: Web Mining; Machine Learning; Clustering and Classification Methods; Pre-Processing and Post-Processing for Data Mining; Optimization

Authors: Harshit Dubey , Saket Bharambe and Vikram Pudi

Affiliation: International Institute of Information Technology - Hyderabad, India

Digital Object Identifier: 10.5220/0004159302580263

Proceedings of KDIR 2012/IC3K, ISBN: 978-989-8565-29-7

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Regression is the study of functional dependency of one variable with respect to other variables. In this paper we propose a novel regression algorithm, BINGR, for predicting dependent variable, having the advantage of low computational complexity. The algorithm is interesting because instead of directly predicting the value of the response variable, it recursively narrows down the range in which response variable lies. BINGR reduces the computation order to logarithmic which is much better than that of existing standard algorithms. As BINGR is parameterless, it can be employed by any naive user. Our experimental study shows that our technique is as accurate as the state of the art, and faster by an order of magnitude.
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Artificial Intelligence

Knowledge-Based Systems

Pre-Processing and Post-Processing for Data Mining

Clustering and Classification Methods

Machine Learning

Soft Computing

Computational Intelligence

Methodologies and Technologies

Symbolic Systems

Evolutionary Computing

Operational Research

Web Mining
Knowledge Discovery and Information Retrieval

Optimization

DETECTING CORRELATIONS BETWEEN HOT DAYS IN NEWS FEEDS

Topics: Web Mining; Mining Text and Semi-Structured Data; Structured Data Analysis and Statistical Methods

Authors: Raghvendra Mall , Nahil Jain and Vikram Pudi

Affiliation: IIIT Hyderabad, India

Digital Object Identifier: 10.5220/0003627203750378

Proceedings of KDIR 2011/IC3K, ISBN: 978-989-8425-79-9

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We use text mining mechanisms to analyze Hot days in news feeds. We build upon the earlier work used to detect Hot topics and assume that we have already attained the Hot days. In this paper we identify the most relevant documents of a topic on a Hot day. We construct a similarity based technique for identifying and ranking these documents. Our aim is to automatically detect chains of hot correlated events over time. We develop a scheme using similarity measures like cosine similarity and KL-divergence to find correlation between these Hot days. For the ‘U.S. Presidential Elections’, the presidential debates which spanned over a week was one such event.
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Artificial Intelligence

Mining Text and Semi-Structured Data

Symbolic Systems

Knowledge Discovery and Information Retrieval

Soft Computing

Web Mining
Knowledge-Based Systems

Structured Data Analysis and Statistical Methods

UNSUPERVISED 3D SEGMENTATION OF HIPPOCAMPUS IN BRAIN MR IMAGES

Topics: Medical Image Detection, Acquisition, Analysis and Processing; Image Analysis and Processing

Authors: Sandeep S. Kaushik and Jayanthi Sivaswamy

Affiliation: IIIT-Hyderabad, India

Digital Object Identifier: 10.5220/0003145401820187

Proceedings of BIOSIGNALS 2011/BIOSTEC, ISBN: 978-989-8425-35-5

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The most widely followed procedure for diagnosis and prognosis of dementia is structural neuroimaging of hippocampus by means of MR. Hippocampus segmentation is of wide interest as it enables quantitative assessment of the structure. In this paper, we propose an algorithm for hippocampus segmentation that is unsupervised and image driven. It is based on a hybrid approach which combines a coarse segmentation and surface evolution. A coarse solution is derived using region growing which is further refined using a modified version of the physics based water flow model (Liu and Nixon, 2007). The proposed method has been tested on a publicly available dataset. The performance of this method is assessed using Dice coefficient against the ground truth provided for 25 volume images. It is consistent across volumes and the average Dice values are comparable to a multi-atlas based method reported on a subset of the same dataset.
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Applications and Services

Biomedical Signal Processing

Medical Image Detection, Acquisition, Analysis and Processing
Biomedical Engineering

Computer Vision, Visualization and Computer Graphics

UTILIZING TERM PROXIMITY BASED FEATURES TO IMPROVE TEXT DOCUMENT CLUSTERING

Topics: Mining Text and Semi-Structured Data; Clustering and Classification Methods

Authors: Shashank Paliwal and Vikram Pudi

Affiliation: International Institute of Information Technology Hyderabad, India

Digital Object Identifier: 10.5220/0003645805370544

Proceedings of SSTM 2011/IC3K, ISBN: 978-989-8425-79-9

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Measuring inter-document similarity is one of the most essential steps in text document clustering. Traditional methods rely on representing text documents using the simple Bag-of-Words (BOW) model which assumes that terms of a text document are independent of each other. Such single term analysis of the text completely ignores the underlying (semantic) structure of a document. In the literature, sufficient efforts have been made to enrich BOW representation using phrases and n-grams like bi-grams and tri-grams. These approaches take into account dependency only between adjacent terms or a continuous sequence of terms. However, while some of the dependencies exist between adjacent words, others are more distant. In this paper, we make an effort to enrich traditional document vector by adding the notion of term-pair features. A Term-Pair feature is a pair of two terms of the same document such that they may be adjacent to each other or distant. We investigate the process of term-pair selection and propose a methodology to select potential term-pairs from the given document. Utilizing term proximity between distant terms also allows some flexibility for two documents to be similar if they are about similar topics but with varied writing styles. Experimental results on standard web document data set show that the clustering performance is substantially improved by adding term-pair features.
Collapse Button Related Ontology Subjects/Areas/Topics (6)
Artificial Intelligence

Knowledge Discovery and Information Retrieval

Mining Text and Semi-Structured Data

Clustering and Classification Methods

Knowledge-Based Systems

Symbolic Systems

SEAR - Scalable, Efficient, Accurate, Robust kNN-based Regression

Topics: Machine Learning; Foundations of Knowledge Discovery in Databases

Authors: Aditya Desai , Himanshu Singh and Vikram Pudi

Affiliation: International Institute of Information Technology, India

Digital Object Identifier: 10.5220/0003068703920395

Proceedings of KDIR 2010/IC3K, ISBN: 978-989-8425-28-7

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Regression algorithms are used for prediction (including forecasting of time-series data), inference, hypothesis testing, and modeling of causal relationships. Statistical approaches although popular, are not generic in that they require the user to make an intelligent guess about the form of the regression equation. In this paper we present a new regression algorithm SEAR – Scalable, Efficient, Accurate kNN-based Regression. In addition to this, SEAR is simple and outlier-resilient. These desirable features make SEAR a very attractive alternative to existing approaches. Our experimental study compares SEAR with fourteen other algorithms on five standard real datasets, and shows that SEAR is more accurate than all its competitors.
Collapse Button Related Ontology Subjects/Areas/Topics (9)
Artificial Intelligence

Foundations of Knowledge Discovery in Databases

Machine Learning

Computational Intelligence

Knowledge Discovery and Information Retrieval

Soft Computing

Evolutionary Computing

Knowledge-Based Systems

Symbolic Systems

A SIMPLE SCHEME FOR CONTOUR DETECTION

Topics: Early Vision and Image Representation; Cognitive & Biologically Inspired Vision

Authors: Gopal Datt Joshi and Jayanthi Sivaswamy

Affiliation: Center for Visual Information Technology, IIIT Hyderabad, India

Digital Object Identifier: 10.5220/0001374702360242

Proceedings of VISAPP 2006/VISIGRAPP, ISBN: 972-8865-40-6

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We present a computationally simple and general purpose scheme for the detection of all salient object contours in real images. The scheme is inspired by the mechanism of surround influence that is exhibited in 80% of neurons in the primary visual cortex of primates. It is based on the observation that the local context of a contour significantly affects the global saliency of the contour. The proposed scheme consists of two steps: first find the edge response at all points in an image using gradient computation and in the second step modulate the edge response at a point by the response in its surround. In this paper, we present the results of implementing this scheme using a Sobel edge operator followed by a mask operation for the surround influence. The proposed scheme has been tested successfully on a large set of images. The performance of the proposed detector compares favourably both computationally and qualitatively, in comparison with another contour detector which is also based on surround influence. Hence, the proposed scheme can serve as a low cost preprocessing step for high level tasks such shape based recognition and image retrieval.
Collapse Button Related Ontology Subjects/Areas/Topics (3)
Computer Vision, Visualization and Computer Graphics

Early Vision and Image Representation

Image and Video Analysis

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