Deep learning and atlas-based models to streamline the segmentation workflow of total marrow and lymphoid irradiation
© 2024. Italian Society of Medical Radiology..
PURPOSE: To improve the workflow of total marrow and lymphoid irradiation (TMLI) by enhancing the delineation of organs at risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models.
MATERIALS AND METHODS: Ninety-five TMLI plans optimized in our institute were analyzed. Two commercial DL software were tested for segmenting 18 OARs. An AB model for lymph node CTV (CTV_LN) delineation was built using 20 TMLI patients. The AB model was evaluated on 20 independent patients, and a semiautomatic approach was tested by correcting the automatic contours. The generated OARs and CTV_LN contours were compared to manual contours in terms of topological agreement, dose statistics, and time workload. A clinical decision tree was developed to define a specific contouring strategy for each OAR.
RESULTS: The two DL models achieved a median [interquartile range] dice similarity coefficient (DSC) of 0.84 [0.71;0.93] and 0.85 [0.70;0.93] across the OARs. The absolute median Dmean difference between manual and the two DL models was 2.0 [0.7;6.6]% and 2.4 [0.9;7.1]%. The AB model achieved a median DSC of 0.70 [0.66;0.74] for CTV_LN delineation, increasing to 0.94 [0.94;0.95] after manual revision, with minimal Dmean differences. Since September 2022, our institution has implemented DL and AB models for all TMLI patients, reducing from 5 to 2 h the time required to complete the entire segmentation process.
CONCLUSION: DL models can streamline the TMLI contouring process of OARs. Manual revision is still necessary for lymph node delineation using AB models.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:129 |
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| Enthalten in: |
La Radiologia medica - 129(2024), 3 vom: 22. März, Seite 515-523 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dei, Damiano [VerfasserIn] |
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| Links: |
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| Themen: |
Atlas-based |
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| Anmerkungen: |
Date Completed 18.03.2024 Date Revised 18.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s11547-024-01760-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 520 | |a © 2024. Italian Society of Medical Radiology. | ||
| 520 | |a PURPOSE: To improve the workflow of total marrow and lymphoid irradiation (TMLI) by enhancing the delineation of organs at risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models | ||
| 520 | |a MATERIALS AND METHODS: Ninety-five TMLI plans optimized in our institute were analyzed. Two commercial DL software were tested for segmenting 18 OARs. An AB model for lymph node CTV (CTV_LN) delineation was built using 20 TMLI patients. The AB model was evaluated on 20 independent patients, and a semiautomatic approach was tested by correcting the automatic contours. The generated OARs and CTV_LN contours were compared to manual contours in terms of topological agreement, dose statistics, and time workload. A clinical decision tree was developed to define a specific contouring strategy for each OAR | ||
| 520 | |a RESULTS: The two DL models achieved a median [interquartile range] dice similarity coefficient (DSC) of 0.84 [0.71;0.93] and 0.85 [0.70;0.93] across the OARs. The absolute median Dmean difference between manual and the two DL models was 2.0 [0.7;6.6]% and 2.4 [0.9;7.1]%. The AB model achieved a median DSC of 0.70 [0.66;0.74] for CTV_LN delineation, increasing to 0.94 [0.94;0.95] after manual revision, with minimal Dmean differences. Since September 2022, our institution has implemented DL and AB models for all TMLI patients, reducing from 5 to 2 h the time required to complete the entire segmentation process | ||
| 520 | |a CONCLUSION: DL models can streamline the TMLI contouring process of OARs. Manual revision is still necessary for lymph node delineation using AB models | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Atlas-based | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Radiotherapy | |
| 650 | 4 | |a Segmentation | |
| 650 | 4 | |a Total marrow and lymphoid irradiation | |
| 650 | 4 | |a Total marrow irradiation | |
| 700 | 1 | |a Lambri, Nicola |e verfasserin |4 aut | |
| 700 | 1 | |a Crespi, Leonardo |e verfasserin |4 aut | |
| 700 | 1 | |a Brioso, Ricardo Coimbra |e verfasserin |4 aut | |
| 700 | 1 | |a Loiacono, Daniele |e verfasserin |4 aut | |
| 700 | 1 | |a Clerici, Elena |e verfasserin |4 aut | |
| 700 | 1 | |a Bellu, Luisa |e verfasserin |4 aut | |
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| 700 | 1 | |a Navarria, Pierina |e verfasserin |4 aut | |
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