Pub Date : 2012-10-04DOI: 10.1109/BIBM.2012.6392657
Ping Zhang, Z. Obradovic
Learning from noisy labels obtained from multiple annotators and without access to any true labels is an increasingly important problem in bioinformatics and biomedicine. In our method, this challenge is addressed by iteratively filtering low-quality annotators and estimating the consensus labels based only on the remaining experts that provide higher-quality annotations. Experiments on biomedical text classification and CASP9 protein disorder prediction tasks provide evidence that the proposed algorithm is more accurate than the majority voting and previously developed multi-annotator approaches. The benefit of using the new method is particularly large when low-quality annotators dominate. Moreover, the new algorithm also suggests the most relevant annotators for each instance, thus paving the way for understanding the behaviors of each annotator and building more reliable predictive models for bioinformatics applications.
{"title":"Integration of multiple annotators by aggregating experts and filtering novices","authors":"Ping Zhang, Z. Obradovic","doi":"10.1109/BIBM.2012.6392657","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392657","url":null,"abstract":"Learning from noisy labels obtained from multiple annotators and without access to any true labels is an increasingly important problem in bioinformatics and biomedicine. In our method, this challenge is addressed by iteratively filtering low-quality annotators and estimating the consensus labels based only on the remaining experts that provide higher-quality annotations. Experiments on biomedical text classification and CASP9 protein disorder prediction tasks provide evidence that the proposed algorithm is more accurate than the majority voting and previously developed multi-annotator approaches. The benefit of using the new method is particularly large when low-quality annotators dominate. Moreover, the new algorithm also suggests the most relevant annotators for each instance, thus paving the way for understanding the behaviors of each annotator and building more reliable predictive models for bioinformatics applications.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79187964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470234
I. Thapa, S. Bhowmick, D. Bastola
Ribosomal RNA sequence is a popular primary molecular target in the diagnosis of many fungal and bacterial infections. More recently a number of other molecular targets like `cytochrome b', `rpoB', `actin' is available in public databases such as GenBank. These sequences could be better alternatives to the popular ribosomal RNA as molecular targets. However, existing computational approaches do not provide a convenient method to collect and make these sequences available for the development of new alternative sequence-based diagnostics that are critical for early detection of infectious agents like fungi. The long-term goal of this study is to develop a computational tool for the rapid identification of infectious agents in biological sample. In the present study, we focus on pre-processing of sequence data in public database and compare a number of clustering approaches to classify currently available DNA sequences into different target genes. We evaluate the correctness of these methods based on the target classification of seven different species of Zygomycetes. Use of a clustering comparison metric has shown that community detection and hierarchical clustering methods are on par with high accuracy.
{"title":"A comparison between hierarchical clustering and community detection method in the collection of gene targets for molecular identification of pathogenic fungi","authors":"I. Thapa, S. Bhowmick, D. Bastola","doi":"10.1109/BIBMW.2012.6470234","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470234","url":null,"abstract":"Ribosomal RNA sequence is a popular primary molecular target in the diagnosis of many fungal and bacterial infections. More recently a number of other molecular targets like `cytochrome b', `rpoB', `actin' is available in public databases such as GenBank. These sequences could be better alternatives to the popular ribosomal RNA as molecular targets. However, existing computational approaches do not provide a convenient method to collect and make these sequences available for the development of new alternative sequence-based diagnostics that are critical for early detection of infectious agents like fungi. The long-term goal of this study is to develop a computational tool for the rapid identification of infectious agents in biological sample. In the present study, we focus on pre-processing of sequence data in public database and compare a number of clustering approaches to classify currently available DNA sequences into different target genes. We evaluate the correctness of these methods based on the target classification of seven different species of Zygomycetes. Use of a clustering comparison metric has shown that community detection and hierarchical clustering methods are on par with high accuracy.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77703680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470263
I. Gkigkitzis
Accurate and computationally inexpensive energy density functional are highly desirable in the simulation of biochemical systems. A molecular "energy" integral functional for the reaction diffusion equation of the triplet oxygen [3O2] in the pseudo state equilibrium during treatment with Photodynamic therapy (PDT) is defined, and its monotonicity is analyzed. When the functional is evaluated on the solution of an existing mathematical model of a spheroid that represents the real physical system of a cell during PDT treatment, it gives a time dependent monotonically decreasing expression energy.
{"title":"Monotonicity functional for a transient mathematical model of oxygen depletion during Photodynamic therapy","authors":"I. Gkigkitzis","doi":"10.1109/BIBMW.2012.6470263","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470263","url":null,"abstract":"Accurate and computationally inexpensive energy density functional are highly desirable in the simulation of biochemical systems. A molecular \"energy\" integral functional for the reaction diffusion equation of the triplet oxygen [3O2] in the pseudo state equilibrium during treatment with Photodynamic therapy (PDT) is defined, and its monotonicity is analyzed. When the functional is evaluated on the solution of an existing mathematical model of a spheroid that represents the real physical system of a cell during PDT treatment, it gives a time dependent monotonically decreasing expression energy.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76396222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470379
Keith S. Decker, Preeti Anday, Liang Sun, C. Schmidt
Pathway models for organisms beyond the most popular model organisms are often notoriously incomplete, even for commercially important species such as gallus gallus. This can make experimental expression data hard to interpret. The paper describes ESCAPE [Experimental System for Curation Assistance of Pathways via Espression data], under development to use available expression data, along with existing orthology mappings and curated machine-interpretable pathways, to assist in rapidly curating new species-specific pathways. Some of these techniques can also be extended to help in the analysis of the expression data in a curated pathway context as well.
{"title":"Using expression data to help pathway curation","authors":"Keith S. Decker, Preeti Anday, Liang Sun, C. Schmidt","doi":"10.1109/BIBMW.2012.6470379","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470379","url":null,"abstract":"Pathway models for organisms beyond the most popular model organisms are often notoriously incomplete, even for commercially important species such as gallus gallus. This can make experimental expression data hard to interpret. The paper describes ESCAPE [Experimental System for Curation Assistance of Pathways via Espression data], under development to use available expression data, along with existing orthology mappings and curated machine-interpretable pathways, to assist in rapidly curating new species-specific pathways. Some of these techniques can also be extended to help in the analysis of the expression data in a curated pathway context as well.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76418120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470330
Lizhi Chen, Feng Yuan, Xiao-Yan Zhao, Mei-ai Liang, W. Fu
The purpose of this work is to investigate the mechanism of Needle-Knife therapy in treating degenerative cervical spondylosis. With the change on the way of life and work, the incidence of degenerative cervical spondylosis is becoming higher and higher, and it has brought great suffering to people. The clinical efficacy of needle-knife therapy in treating the degenerative cervical spondylosis is satisfactory. Needle-knife therapy is an emerging treatmenti but it shows a significant effect in treating the degenerative cervical spondylosis. We will investigate the mechanism of Needle-Knife therapy in treating degenerative cervical spondylosis from spinal biomechanics and soft tissue injury.
{"title":"Mechanism of needle-knife therapy in treating degenerative cervical spondylosis","authors":"Lizhi Chen, Feng Yuan, Xiao-Yan Zhao, Mei-ai Liang, W. Fu","doi":"10.1109/BIBMW.2012.6470330","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470330","url":null,"abstract":"The purpose of this work is to investigate the mechanism of Needle-Knife therapy in treating degenerative cervical spondylosis. With the change on the way of life and work, the incidence of degenerative cervical spondylosis is becoming higher and higher, and it has brought great suffering to people. The clinical efficacy of needle-knife therapy in treating the degenerative cervical spondylosis is satisfactory. Needle-knife therapy is an emerging treatmenti but it shows a significant effect in treating the degenerative cervical spondylosis. We will investigate the mechanism of Needle-Knife therapy in treating degenerative cervical spondylosis from spinal biomechanics and soft tissue injury.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81268294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470371
Shun Long, Guo-ming Chen, Weiheng Zhu, Wen-juan Shi
Knee arthritis is a common disease for elderly people who suffer inflammation of their knees. Various remedies have been proposed and put into practice for knee arthritis treatment and their efficacies vary. We present in this paper an empirical study on various long-established remedies for arthritis. The results given by various analysese we applied are not as clear-cut as expected. Some suggest that some common beliefs are incorrect, whilst others give controversy results hard to explain. These results can serve as a useful guide and reference for further study in related areas.
{"title":"An empirical study on knee arthritis remedies","authors":"Shun Long, Guo-ming Chen, Weiheng Zhu, Wen-juan Shi","doi":"10.1109/BIBMW.2012.6470371","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470371","url":null,"abstract":"Knee arthritis is a common disease for elderly people who suffer inflammation of their knees. Various remedies have been proposed and put into practice for knee arthritis treatment and their efficacies vary. We present in this paper an empirical study on various long-established remedies for arthritis. The results given by various analysese we applied are not as clear-cut as expected. Some suggest that some common beliefs are incorrect, whilst others give controversy results hard to explain. These results can serve as a useful guide and reference for further study in related areas.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79575084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-04DOI: 10.1109/BIBM.2012.6392697
Kang Li, Nan Du, A. Zhang
Informative Gene Selection is the process of identifying relevant genes that are significantly and differentially expressed in biological procedures. The microarray experiments conducted for this purpose usually implement only less than a hundred of samples to rank the relevance of over thousands of genes. Many irrelevant genes thus may gain statistical importance due to the randomness caused by the small sample problem, while relevant genes may lose focus in the same way. Overcoming such a problem goes beyond what a single microarray dataset can offer and stresses the use of multiple experiment results, which is defined as rank aggregation. In this paper, we propose a novel link prediction based rank aggregation algorithm for the purpose of informative gene selection. Each rank is transferred into a fully connected and weighted network, in which the nodes represent genes and the weights of links stand for priorities between connected nodes (genes). The integration of multiple gene ranks is then formulated as an optimization problem of link prediction on multiple networks, with criterion function favoring the maximization of weighted consensus among each network. We solve the problem through iterative estimation of weights and maximization of consensus among them. In the experimental evaluation, we demonstrate our method on the Prostate Cancer Dataset and compare it with other baseline methods. The results show that our link prediction based rank aggregation method remarkably outperforms all the compared methods, which proves the effectiveness of our framework in finding informative genes from multiple microarray experimental results.
{"title":"A link prediction based unsupervised rank aggregation algorithm for informative gene selection","authors":"Kang Li, Nan Du, A. Zhang","doi":"10.1109/BIBM.2012.6392697","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392697","url":null,"abstract":"Informative Gene Selection is the process of identifying relevant genes that are significantly and differentially expressed in biological procedures. The microarray experiments conducted for this purpose usually implement only less than a hundred of samples to rank the relevance of over thousands of genes. Many irrelevant genes thus may gain statistical importance due to the randomness caused by the small sample problem, while relevant genes may lose focus in the same way. Overcoming such a problem goes beyond what a single microarray dataset can offer and stresses the use of multiple experiment results, which is defined as rank aggregation. In this paper, we propose a novel link prediction based rank aggregation algorithm for the purpose of informative gene selection. Each rank is transferred into a fully connected and weighted network, in which the nodes represent genes and the weights of links stand for priorities between connected nodes (genes). The integration of multiple gene ranks is then formulated as an optimization problem of link prediction on multiple networks, with criterion function favoring the maximization of weighted consensus among each network. We solve the problem through iterative estimation of weights and maximization of consensus among them. In the experimental evaluation, we demonstrate our method on the Prostate Cancer Dataset and compare it with other baseline methods. The results show that our link prediction based rank aggregation method remarkably outperforms all the compared methods, which proves the effectiveness of our framework in finding informative genes from multiple microarray experimental results.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77323416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470221
Nikolaos S. Alachiotis, S. Berger, T. Flouri, S. Pissis, A. Stamatakis
A broad variety of short-read alignment programmes has been released recently to address the task of mapping tens of millions of short reads to a reference genome, placing emphasis on various aspects of the problem. Although all programmes allow for a small number of alignment mismatches, some of them either perform poorly when allowing gap insertions or they do not allow for gap insertions at all. The seed-and-extend strategy is applied in most of these programmes: after a fast alignment between a fragment of the reference sequence and a high-quality fragment of a short read-the seed-an important problem is to extend the alignment between a relatively short succeeding fragment of the reference sequence and the remaining low-quality fragment of the read allowing a number of mismatches and the insertion of gaps in the alignment. However, the length of the short reads in combination with the gap occurrence frequency observed in various applications suggest that the single-gap alignment of (parts of) those reads is desirable. In this article, we present libgapmis, an ultrafast library for pairwise short-read single-gap alignment including accelerated SSE-based and GPU-based versions. It implements an algorithm, which computes a modified version of the traditional dynamic programming matrix for sequence alignment to solve the above alignment problem. We show that the library functions of the CPU-based version are up to 20x faster compared to competing programmes, while the respective SSE-based and GPU-based versions are up to 6x and llx faster than our CPU-based implementation, respectively. The functions made available via our library can be seamlessly integrated into any short-read alignment pipeline.
{"title":"Libgapmis: An ultrafast library for short-read single-gap alignment","authors":"Nikolaos S. Alachiotis, S. Berger, T. Flouri, S. Pissis, A. Stamatakis","doi":"10.1109/BIBMW.2012.6470221","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470221","url":null,"abstract":"A broad variety of short-read alignment programmes has been released recently to address the task of mapping tens of millions of short reads to a reference genome, placing emphasis on various aspects of the problem. Although all programmes allow for a small number of alignment mismatches, some of them either perform poorly when allowing gap insertions or they do not allow for gap insertions at all. The seed-and-extend strategy is applied in most of these programmes: after a fast alignment between a fragment of the reference sequence and a high-quality fragment of a short read-the seed-an important problem is to extend the alignment between a relatively short succeeding fragment of the reference sequence and the remaining low-quality fragment of the read allowing a number of mismatches and the insertion of gaps in the alignment. However, the length of the short reads in combination with the gap occurrence frequency observed in various applications suggest that the single-gap alignment of (parts of) those reads is desirable. In this article, we present libgapmis, an ultrafast library for pairwise short-read single-gap alignment including accelerated SSE-based and GPU-based versions. It implements an algorithm, which computes a modified version of the traditional dynamic programming matrix for sequence alignment to solve the above alignment problem. We show that the library functions of the CPU-based version are up to 20x faster compared to competing programmes, while the respective SSE-based and GPU-based versions are up to 6x and llx faster than our CPU-based implementation, respectively. The functions made available via our library can be seamlessly integrated into any short-read alignment pipeline.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91288929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-04DOI: 10.1109/BIBM.2012.6392662
Ming Li
Protein structure prediction by computers at best may serve as a screening method, and the current high-throughput protein structure determination methods are costly and will never exhaust all proteins. A complementary approach is "protein structure determination on demand", say in a week. We will discuss two approaches that would realize this goal: automatic protein structure determination using NMR data and mass spectrometry data.
{"title":"(2) Protein structure determination on demand","authors":"Ming Li","doi":"10.1109/BIBM.2012.6392662","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392662","url":null,"abstract":"Protein structure prediction by computers at best may serve as a screening method, and the current high-throughput protein structure determination methods are costly and will never exhaust all proteins. A complementary approach is \"protein structure determination on demand\", say in a week. We will discuss two approaches that would realize this goal: automatic protein structure determination using NMR data and mass spectrometry data.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90608230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-04DOI: 10.1109/BIBMW.2012.6470226
Sharmili Roy, M. S. Brown, G. Shih
In a typical radiological reporting workflow, radiologists make image-based annotations to denote regions of clinical significance or to perform quantitative measurements. Interestingly, virtually all annotation software allow only 2D geometric primitives such as line segments and ellipses; 3D volume annotation is not supported. As a result, when dealing with anatomic entities that have volumetric properties (e.g. tumors, organs), a radiologist must summarize volumetric quantities in a written text-report or use a third party software outside the standard workflow to perform volumetric segmentation. In this paper, we describe an automated method to extract volumes from radiological annotations. Specifically, we describe a clustering method that parses the annotations of unconnected line segments to determine the locations of volumes. We show how this extracted information can be used to bootstrap and accelerate subsequent 3D segmentation while avoiding the need to perform redundant markup or segmentation seeding outside the standard radiological workflow. This 3D data can be utilized to enhance important clinical applications such as radiological reporting, exam summarization and visualization.
{"title":"Extracting volumetric information from standard two-dimensional radiological annotations within the clinical workflow","authors":"Sharmili Roy, M. S. Brown, G. Shih","doi":"10.1109/BIBMW.2012.6470226","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470226","url":null,"abstract":"In a typical radiological reporting workflow, radiologists make image-based annotations to denote regions of clinical significance or to perform quantitative measurements. Interestingly, virtually all annotation software allow only 2D geometric primitives such as line segments and ellipses; 3D volume annotation is not supported. As a result, when dealing with anatomic entities that have volumetric properties (e.g. tumors, organs), a radiologist must summarize volumetric quantities in a written text-report or use a third party software outside the standard workflow to perform volumetric segmentation. In this paper, we describe an automated method to extract volumes from radiological annotations. Specifically, we describe a clustering method that parses the annotations of unconnected line segments to determine the locations of volumes. We show how this extracted information can be used to bootstrap and accelerate subsequent 3D segmentation while avoiding the need to perform redundant markup or segmentation seeding outside the standard radiological workflow. This 3D data can be utilized to enhance important clinical applications such as radiological reporting, exam summarization and visualization.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85985853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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