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"Autocontouring of the mouse thorax using deep learning"
Justin Malimban, Danny Lathouwers, Haibin Qian, Frank Verhaegen, Julia Wiedemann, Sytze Brandenburg and Marius Staring
Abstract
Image-guided small animal irradiations are typically performed in a single session, requiring continuous administration of anesthesia. Prolonged exposure to anesthesia can potentially affect experimental outcomes and thus, a fast preclinical irradiation workflow is desired. Similar to the clinic, delineation of organs remains one of the most time-consuming and labor-intensive stages in the preclinical workflow, and this is amplified by the fact that hundreds of animals are involved in a single study. In this work, we evaluated the accuracy and efficiency of deep learning pipelines for automated contouring of organs in the mouse thorax.
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Copyright © 2022 by the authors.
Published version © 2022 by .
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @article{Malimban:2022, |
| author |
= {Malimban, Justin and Lathouwers, Danny and Qian, Haibin and Verhaegen, Frank and Wiedemann, Julia and Brandenburg, Sytze and Staring, Marius}, |
| title |
= {Autocontouring of the mouse thorax using deep learning}, |
| journal |
= {Radiotherapy and Oncology (ESTRO)}, |
| month |
= {May}, |
| year |
= {2022}, |
| } |
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| last modified: 15-08-2022 |
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