FIST: Fast Interactive Segmentation of Tumors

Steger, Sebastian; Sakas, Georgios

doi:10.1007/978-3-642-28557-8_16

Cited by 17 publications

(10 citation statements)

References 10 publications

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“…The tumor segmentation plugin provides two tools to segment tumors. The first one is an interactive 3D radial ray based approach, which uses belief propagation to optimize the contours [13]. The second one is a graph-cut based approach [5].…”

Section: Overviewmentioning

confidence: 99%

Multiphase risk assessment of atypical liver resections

Drechsler

Erdt

Laura

et al. 2012

2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

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In this work, we present a system based on open-source toolkits that can utilize multiple phases of a liver CT dataset to plan a surgery before the patient enters the operation room. The contributions are an optimized deformable registration of arterial and venous phases of the liver and a methodology to assess surgical risk utilizing both phases. It is shown by the example of two clinical cases, how our system enables the fusion of complementary information into a 3D representation of the patient anatomy. Processing times of single processing steps on a modern machine are quite low, which allows for an integration into clinical routine

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Section: Overviewmentioning

confidence: 99%

Multiphase risk assessment of atypical liver resections

Drechsler

Erdt

Laura

et al. 2012

2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

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“…An articulated atlas is used to extract individual bones in the CT image ( Fig. 9) (Steger and Sakas 2012;Steger and Kirschner 2012). For each of the bones, a single rigid transformation is successively found based on the maximization of an image similarity metric.…”

Section: Image Fusionmentioning

confidence: 99%

Biomarkers in NeoMark European Project for Oral Cancers

Poli¹,

Copelli²,

Lanfranco³

et al. 2014

Biomarkers in Cancer

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Oral cavity cancers are the seventh tumor by diffusion worldwide with more than 90 % being diagnosed as oral squamous cell carcinomas (OSCCs). According to the latest WHO statistics, OSCC accounts for 5 % of the cancer deaths worldwide, being the eighth more lethal cancer entity. Early identification of cancer relapses would have the potentiality to improve the disease control and the patient survival.NeoMark is a European co-funded research project (Seventh Framework Program, Information and Communication Technologies: EU-FP7-ICT-2007-2-22483-NeoMark) that has the objective to identify relevant biomarkers of OSCC recurrence. It integrates high-throughput gene expression analysis in tumor cells and IT-assisted imaging with traditional staging and follow-up protocols to improve the recurrence risk stratification and to obtain the earlier identification of locoregional relapses.The architecture of the project is based on the following key points:-Creation of a web application tool: a unified interface that helps the storage and management of all information -NeoMark database: the heterogeneous NeoMark data (demographics and risk factors; clinical, pathological, and immunohistochemical parameters; filtered and cleaned genomic and imaging data) are stored in a single database -the Integrated Health Record Repository (IHRR) -on a central NeoMark server. The server contains the marker definition functional environment (MDFE), a data analysis module. Based on the heterogeneous input data, it estimates the likelihood of a relapse and identifies OSCC risk factors. -Imaging biomarker extraction: several biomarkers are obtained from medical images such as CT and MRI scans (size, amount of necrosis from tumor and lymph nodes, etc.). To extract those features, a custom software tool -called the NeoMark Image Processing Tool -has specifically been developed. -Genomic data cleaning and filtering: extraction of genomic data and filtering of genes with low data quality and of those with high number of missing values.The NeoMark system was trained and initially validated in a multicenter pilot study (three European clinical centers involved: two in Italy and one in Spain) basing on 86 patients affected by OSCC with a minimum follow-up of 12 months. The clinicians recognized the usefulness of the disease bioprofile (or disease-specific profile) identified by NeoMark to evaluate the risk of disease reoccurrence of a patient at diagnosis, to stratify patients affected by OSCC at baseline according to the risk of recurrence, and to reserve a "tailored therapy" to each case. List of Abbreviations Key Facts• Oral squamous cell carcinoma (OSCC): it accounts for 5 % of the cancer deaths worldwide, being the eighth more lethal cancer entity.• Tumor relapse that afflicts 25-50 % of the surgically treated OSCC patients represents the main cause of failure and death.• Early identification of OSCC relapses can improve patient survival and quality of life.• The state of the art in OSCC patient classification is mainly based on TNM stag...

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“…Starting from there, a radial ray-based segmentation technique is used to segment the lymph nodes automatically [6]. Due to the unpredictability of the shape, appearance, and surrounding tissue of the tumor, an interactive approach is used for tumor segmentation [7].…”

Section: ) Integrated Health Record Repository (Ihrr)mentioning

confidence: 99%

Merging Person-Specific Bio-Markers for Predicting Oral Cancer Recurrence Through an Ontology

Salvi

Picone

Arredondo

et al. 2013

IEEE Trans. Biomed. Eng.

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Abstract-One of the major problems related to cancer treatment is its recurrence. Without knowing in advance how likely the cancer will relapse, clinical practice usually recommends adjuvant treatments that have strong side effects. A way to optimize treatments is to predict the recurrence probability by analyzing a set of bio-markers. The NeoMark European project has identified a set of preliminary bio-markers for the case of oral cancer by collecting a large series of data from genomic, imaging, and clinical evidence. This heterogeneous set of data needs a proper representation in order to be stored, computed, and communicated efficiently. Ontologies are often considered the proper mean to integrate biomedical data, for their high level of formality and for the need of interoperable, universally accepted models. This paper presents the NeoMark system and how an ontology has been designed to integrate all its heterogeneous data. The system has been validated in a pilot in which data will populate the ontology and will be made public for further research.

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FIST: Fast Interactive Segmentation of Tumors

Cited by 17 publications

References 10 publications

Multiphase risk assessment of atypical liver resections

Multiphase risk assessment of atypical liver resections

Biomarkers in NeoMark European Project for Oral Cancers

Merging Person-Specific Bio-Markers for Predicting Oral Cancer Recurrence Through an Ontology

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