A feasibility study of lung tumor segmentation on kilo-voltage radiographic images with transfer learning: Toward tumor motion tracking in radiotherapy.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

PMID: 40023957

Abstract

PURPOSE: To segment the lung tumor on kilo-voltage X-ray radiographic images acquired during treatment toward the markerless lung tumor tracking.

Authors

Jie Wei

Department of Computer Science, City College of New York, New York, USA.
Jun Deng

Department of Therapeutic Radiology, Yale University, New Haven, CT, U.S.A.
Ming Chao

Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Keywords

Algorithms Deep Learning Feasibility Studies Humans Image Processing, Computer-Assisted Lung Neoplasms Movement Radiotherapy, Image-Guided

External Resources

View on PubMed Access via DOI PubMed (40023957)

A feasibility study of lung tumor segmentation on kilo-voltage radiographic images with transfer learning: Toward tumor motion tracking in radiotherapy.

Abstract

Authors

Keywords

External Resources

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