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Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation

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Head and Neck Tumor Segmentation and Outcome Prediction (HECKTOR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13626))

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Abstract

Head and Neck (H &N) organ-at-risk (OAR) and tumor segmentations are an essential component of radiation therapy planning. The varying anatomic locations and dimensions of H &N nodal Gross Tumor Volumes (GTVn) and H &N primary gross tumor volume (GTVp) are difficult to obtain due to lack of accurate and reliable delineation methods. The downstream effect of incorrect segmentation can result in unnecessary irradiation of normal organs. Towards a fully automated radiation therapy planning algorithm, we explore the efficacy of multi-scale fusion based deep learning architectures for accurately segmenting H &N tumors from medical scans. Team Name: M &H_lab_NU.

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Acknowledgment

This project is supported by the NIH funding: R01-CA246704 and R01-CA240639. The computations in this paper were performed on equipment provided by the Experimental Infrastructure for Exploration of Exascale Computing (eX3), which is financially supported by the Research Council of Norway under contract 270053.

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Authors and Affiliations

  1. Machine and Hybrid Intelligence Lab, Department of Radiology, Northwestern University, Chicago, USA

    Abhishek Srivastava, Debesh Jha & Ulas Bagci

  2. Department of Radiation Oncology, University of Chicago, Chicago, IL, USA

    Bulent Aydogan

  3. Department of Radiation Oncology, Northwestern University, Chicago, IL, USA

    Mohamed E. Abazeed

Authors
  1. Abhishek Srivastava
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  2. Debesh Jha
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  3. Bulent Aydogan
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  4. Mohamed E. Abazeed
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  5. Ulas Bagci
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Corresponding author

Correspondence to Abhishek Srivastava .

Editor information

Editors and Affiliations

  1. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Vincent Andrearczyk

  2. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Valentin Oreiller

  3. LaTIM, INSERM, University of Brest, Brest, France

    Mathieu Hatt

  4. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Adrien Depeursinge

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Srivastava, A., Jha, D., Aydogan, B., Abazeed, M.E., Bagci, U. (2023). Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2022. Lecture Notes in Computer Science, vol 13626. Springer, Cham. https://doi.org/10.1007/978-3-031-27420-6_11

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  • DOI: https://doi.org/10.1007/978-3-031-27420-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27419-0

  • Online ISBN: 978-3-031-27420-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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