Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

2020 ◽  
Vol 47 (9) ◽  
Author(s):  
Tomaž Vrtovec ◽  
Domen Močnik ◽  
Primož Strojan ◽  
Franjo Pernuš ◽  
Bulat Ibragimov
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Head And Neck ◽  
Organs At Risk ◽  
Radiotherapy Planning ◽  
Learning Methods ◽  
Head And Neck Radiotherapy

Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation

2019 ◽  
Vol 104 (3) ◽  
pp. 677-684 ◽  
Author(s):  
Ward van Rooij ◽  
Max Dahele ◽  
Hugo Ribeiro Brandao ◽  
Alexander R. Delaney ◽  
Berend J. Slotman ◽  
...  
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Head And Neck ◽  
Organs At Risk

scholarly journals Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

10.2196/26151 ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. e26151
Author(s):  
Stanislav Nikolov ◽  
Sam Blackwell ◽  
Alexei Zverovitch ◽  
Ruheena Mendes ◽  
Michelle Livne ◽  
...  
Keyword(s):  
Computed Tomography ◽  
At Risk ◽  
Deep Learning ◽  
Clinical Practice ◽  
Head And Neck ◽  
Organs At Risk ◽  
Data Set ◽  
Computed Tomography Scans ◽  
Neck Anatomy ◽  
Organ At Risk

Background Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

scholarly journals Benefits of deep learning for delineation of organs at risk in head and neck cancer

2019 ◽  
Vol 138 ◽  
pp. 68-74 ◽  
Author(s):  
J. van der Veen ◽  
S. Willems ◽  
S. Deschuymer ◽  
D. Robben ◽  
W. Crijns ◽  
...  
Keyword(s):  
At Risk ◽  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Neck Cancer ◽  
Organs At Risk

scholarly journals Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy

2020 ◽  
Vol 16 ◽  
pp. 54-60
Author(s):  
Charlotte L. Brouwer ◽  
Djamal Boukerroui ◽  
Jorge Oliveira ◽  
Padraig Looney ◽  
Roel J.H.M. Steenbakkers ◽  
...  
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Clinical Practice ◽  
Head And Neck ◽  
Organs At Risk ◽  
Manual Adjustment

scholarly journals External validation of deep learning-based contouring of head and neck organs at risk

2020 ◽  
Vol 15 ◽  
pp. 8-15 ◽  
Author(s):  
Ellen J.L. Brunenberg ◽  
Isabell K. Steinseifer ◽  
Sven van den Bosch ◽  
Johannes H.A.M. Kaanders ◽  
Charlotte L. Brouwer ◽  
...  
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Head And Neck ◽  
Organs At Risk ◽  
External Validation

scholarly journals OC-0423: An evaluation of variabilities in organs-at-risk delineation for MR-only head and neck radiotherapy

2018 ◽  
Vol 127 ◽  
pp. S221-S222
Author(s):  
K.Y. Chui ◽  
W.W.K. Fung ◽  
J. Yuan ◽  
A.W.L. Mui ◽  
G. Chiu
Keyword(s):  
At Risk ◽  
Head And Neck ◽  
Organs At Risk ◽  
Head And Neck Radiotherapy
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