Automatic Segmentation with Deep Learning in Radiotherapy
This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover common...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/15/17/4389 |
| _version_ | 1797582733710458880 |
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| author | Lars Johannes Isaksson Paul Summers Federico Mastroleo Giulia Marvaso Giulia Corrao Maria Giulia Vincini Mattia Zaffaroni Francesco Ceci Giuseppe Petralia Roberto Orecchia Barbara Alicja Jereczek-Fossa |
| author_facet | Lars Johannes Isaksson Paul Summers Federico Mastroleo Giulia Marvaso Giulia Corrao Maria Giulia Vincini Mattia Zaffaroni Francesco Ceci Giuseppe Petralia Roberto Orecchia Barbara Alicja Jereczek-Fossa |
| author_sort | Lars Johannes Isaksson |
| collection | DOAJ |
| description | This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: “What should researchers think about when starting a segmentation study?”, “How can research practices in medical image segmentation be improved?”, “What is missing from the current corpus?”, and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today’s competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information. |
| first_indexed | 2024-03-10T23:26:43Z |
| format | Article |
| id | doaj.art-64399d49c3044b90967637cff472abc6 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T23:26:43Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-64399d49c3044b90967637cff472abc62023-11-19T07:57:05ZengMDPI AGCancers2072-66942023-09-011517438910.3390/cancers15174389Automatic Segmentation with Deep Learning in RadiotherapyLars Johannes Isaksson0Paul Summers1Federico Mastroleo2Giulia Marvaso3Giulia Corrao4Maria Giulia Vincini5Mattia Zaffaroni6Francesco Ceci7Giuseppe Petralia8Roberto Orecchia9Barbara Alicja Jereczek-Fossa10Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 Milan, ItalyDivision of Radiology, IEO European Institute of Oncology IRCCS, 20141 Milan, ItalyDivision of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 Milan, ItalyDivision of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 Milan, ItalyDivision of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 Milan, ItalyDivision of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 Milan, ItalyDivision of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 Milan, ItalyDepartment of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, ItalyDepartment of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, ItalyScientific Directorate, IEO European Institute of Oncology IRCCS, 20141 Milan, ItalyDivision of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 Milan, ItalyThis review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: “What should researchers think about when starting a segmentation study?”, “How can research practices in medical image segmentation be improved?”, “What is missing from the current corpus?”, and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today’s competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information.https://www.mdpi.com/2072-6694/15/17/4389radiotherapysegmentationautomaticdeep learningartificial intelligenceartificial neural networks |
| spellingShingle | Lars Johannes Isaksson Paul Summers Federico Mastroleo Giulia Marvaso Giulia Corrao Maria Giulia Vincini Mattia Zaffaroni Francesco Ceci Giuseppe Petralia Roberto Orecchia Barbara Alicja Jereczek-Fossa Automatic Segmentation with Deep Learning in Radiotherapy Cancers radiotherapy segmentation automatic deep learning artificial intelligence artificial neural networks |
| title | Automatic Segmentation with Deep Learning in Radiotherapy |
| title_full | Automatic Segmentation with Deep Learning in Radiotherapy |
| title_fullStr | Automatic Segmentation with Deep Learning in Radiotherapy |
| title_full_unstemmed | Automatic Segmentation with Deep Learning in Radiotherapy |
| title_short | Automatic Segmentation with Deep Learning in Radiotherapy |
| title_sort | automatic segmentation with deep learning in radiotherapy |
| topic | radiotherapy segmentation automatic deep learning artificial intelligence artificial neural networks |
| url | https://www.mdpi.com/2072-6694/15/17/4389 |
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