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MICCAI-AIRT Workshop Call For Papers

 

MICCAI Workshop on Artificial Intelligence in Radiation Therapy 2019

MICCI-AIRT

17 October 2019

  

Organizers: 

 Steve Jiang, Ph.D.

Medical Artificial Intelligence and Automation (MAIA) Laboratory

Department of Radiation Oncology

UT Southwestern Medical Center

 Dan Nguyen, Ph.D.

Medical Artificial Intelligence and Automation (MAIA) Laboratory

Department of Radiation Oncology

UT Southwestern Medical Center

 Lei Xing,
Ph.D.

Laboratory for
Artificial Intelligence in Medicine and Biomedical Physics

Department of Radiation Oncology

Stanford Medicine 

 

Workshop Description:

The workshop will be focused on the application of artificial intelligence (AI) and automation technologies in radiation therapy. With this workshop, we hope to open a discussion about the state of radiation therapy, the state of AI and related technologies, and pathway to revolutionizing the field to ultimately improve cancer patient outcome and quality of life. We believe that in working with the intelligent minds at MICCAI, the field of radiation therapy with greatly benefit from the exposure of the latest cutting-edge algorithms, and MICCAI will grow from tackling the unique challenges in radiation therapy.

In particular, we will focus on the application/development AI and related technologies in 2 fronts: 1) image guided treatment delivery, and 2) image guided treatment strategy. Image guided treatment delivery will be focused on advancements of technologies that are used during the delivery of the radiation to the patient for image guided radiation therapy (IGRT), which includes developments in cone beam computed tomography (CBCT), fluoroscopy, surface imaging, motion management, and other modalities that are used for IGRT. Image guided treatment strategy will involve technologies that are used in the clinical pipeline leading up to the delivery, which include segmentation techniques and algorithms on CT, MRI, and/or PET, treatment planning, dose calculation, quality assurance and error detection, etc.

CBCT, fluoroscopy, surface imaging, and related submissions for image guided treatment delivery will focus on the use of the imaging modalities for accurate and precise delivery of the planned radiation dose onto the tumor and healthy tissue. Motion management includes immobilization methods and imaging for motion verification or prediction. Segmentation related submissions will focus on the segmentation that is specific to the radiotherapy pipeline, and may use CT, MRI, and/or PET images for algorithm development. Treatment planning submissions will focus on techniques and algorithms for improving the plan quality and/or the planning efficiency. Dose calculation related submissions may focus on photon, electron, protons, or heavy ion, with applications to radiation therapy. Quality assurance and error detection submissions including ensuring the calculated dose matches the delivered dose, identifying human mistakes during treatment planning/delivery, incident learning, risk analysis, and process control.

We believe that the MICCAI workshop for AI in radiation therapy is the perfect platform for providing discussion of the state of radiation therapy, the state of AI and related technologies, and pathway to revolutionizing the field to ultimately improve cancer patient outcome and quality of life.

 

Call for Papers:

Please submit your papers here: https://easychair.org/cfp/miccai-airt2019

The submissions are to be focused on either image guided treatment delivery and image guided treatment strategy. Image guided treatment delivery includes advancements of technologies that are used during the delivery of the radiation to the patient for image guided radiation therapy (IGRT), which includes developments in cone beam computed tomography (CBCT), fluoroscopy, surface imaging, motion management, and other modalities that are used for IGRT. Image guided treatment strategy includes technologies that are used in the clinical pipeline leading up to the delivery, which include segmentation techniques and algorithms on CT, MRI, and/or PET, treatment planning, dose calculation, quality assurance and error detection, etc.

The particular algorithms and technologies in the submission can include deep learning, machine learning, and automation methods, such as neural networks, support vector machine, regression, decision trees and forests, dimensionality reduction algorithms, computational/mathematical optimization techniques, etc., and may cover any of the learning methods, including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, etc. While we expect the majority of submissions to be focused on using deep learning technologies for the aforementioned topics, we are open and encourage submissions that may explore the usage of novel technologies that fall under the umbrella of “artificial intelligence and automation”

 

Important Dates:

Deadline to Submit: 5 August 2019

Notification of Acceptance: 12 August 2019

Workshop Date: 17 October 2019

 

Submission Instructions:

Papers should be submitted electronically following the guidelines for authors and LaTeX and MS Word templates available at Lecture Notes in Computer Science, double blind review). Manuscripts should be up to 8-pages. No modifications to the templates are permitted. Failure to abide by the formatting guidelines will result in immediate rejection of the paper. The papers will be evaluated by external reviewers or potential inclusion in the scientific program.