A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation.
Algorithm
Contour
Deep-image-to-image-network
Organs at risk
Pelvis
Planning
Radiation
Thorax
Journal
Radiation oncology (London, England)
ISSN: 1748-717X
Titre abrégé: Radiat Oncol
Pays: England
ID NLM: 101265111
Informations de publication
Date de publication:
22 Jul 2022
22 Jul 2022
Historique:
received:
14
03
2022
accepted:
28
06
2022
entrez:
22
7
2022
pubmed:
23
7
2022
medline:
27
7
2022
Statut:
epublish
Résumé
We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning. The algorithm identifies the region of interest (ROI) automatically by detecting anatomical landmarks around the specific organs using a deep reinforcement learning technique. The segmentation is restricted to this ROI and performed by a deep image-to-image network (DI2IN) based on a convolutional encoder-decoder architecture combined with multi-level feature concatenation. The algorithm is commercially available in the medical products "syngo.via RT Image Suite VB50" and "AI-Rad Companion Organs RT VA20" (Siemens Healthineers). For evaluation, thoracic CT images of 237 patients and pelvic CT images of 102 patients were manually contoured following the Radiation Therapy Oncology Group (RTOG) guidelines and compared to the DI2IN results using metrics for volume, overlap and distance, e.g., Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD We observed high correlations between automatic and manual contours. The best results were obtained for the lungs (DSC 0.97, HD The DI2IN algorithm automatically generated contours for organs at risk close to those by a human expert, making the contouring step in radiation treatment planning simpler and faster. Few cases still required manual corrections, mainly for heart and rectum.
Sections du résumé
BACKGROUND
BACKGROUND
We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning.
METHODS
METHODS
The algorithm identifies the region of interest (ROI) automatically by detecting anatomical landmarks around the specific organs using a deep reinforcement learning technique. The segmentation is restricted to this ROI and performed by a deep image-to-image network (DI2IN) based on a convolutional encoder-decoder architecture combined with multi-level feature concatenation. The algorithm is commercially available in the medical products "syngo.via RT Image Suite VB50" and "AI-Rad Companion Organs RT VA20" (Siemens Healthineers). For evaluation, thoracic CT images of 237 patients and pelvic CT images of 102 patients were manually contoured following the Radiation Therapy Oncology Group (RTOG) guidelines and compared to the DI2IN results using metrics for volume, overlap and distance, e.g., Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD
RESULTS
RESULTS
We observed high correlations between automatic and manual contours. The best results were obtained for the lungs (DSC 0.97, HD
CONCLUSIONS
CONCLUSIONS
The DI2IN algorithm automatically generated contours for organs at risk close to those by a human expert, making the contouring step in radiation treatment planning simpler and faster. Few cases still required manual corrections, mainly for heart and rectum.
Identifiants
pubmed: 35869525
doi: 10.1186/s13014-022-02102-6
pii: 10.1186/s13014-022-02102-6
pmc: PMC9308364
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
129Subventions
Organisme : Bavarian Ministry of Economic Affairs, Regional Development and Energy
ID : 41-6618c/325/2-MED-1610-0003
Organisme : Bavarian Ministry of Economic Affairs, Regional Development and Energy
ID : 41-6618c/324/2-MED-1610-0002
Organisme : Bavarian Ministry of Economic Affairs, Regional Development and Energy
ID : 41-6618c/324/2-MED-1610-0002
Organisme : Bavarian Ministry of Economic Affairs, Regional Development and Energy
ID : 41-6618c/325/2-MED-1610-0003
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s).
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