JMRS ASMIRT CPD Q&A - Radiation Therapy -  2023 Special Issue S1 Horizon Scanning
Maximise your CPD by reading the following selected Radiation Therapy article and answer the five questions.
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Continuing Professional Development Q&A - Radiation Therapy
Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy

Gibbons E, Hoffmann M, Westhuyzen J, Hodgson A, Chick B, Last A. 

(2023) J Med Radiat Sci.    

1.     How are deep learning (DL) auto-segmentation contours produced on an incoming dataset? *
1 point
2.     Which of the following is correct in terms of the number of training datasets used for atlas and DL segmentation? *
1 point
3.     Which DL-based OARs experienced statistically significant time improvements over atlas-based OARs? *
1 point
4.     For auto-segmented OAR contours, it is recommended that: *
1 point
5.     Why can vendor trained 'generic' DL models be beneficial to a radiation therapy workflow? *
1 point
Recommended further reading:

1.   Brouwer CL, Boukerroui D, Oliveira J, Looney P, Steenbakkers RJHM, Langendijk JA, Both S, Gooding MJ. Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy. Phys Imaging Radiat Oncol. 2020 Oct 14;16:54-60.

2.   Sherer MV, Lin D, Elguindi S, Duke S, Tan LT, Cacicedo J, Dahele M, Gillespie EF. Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiother Oncol. 2021 Jul;160:185-191.

3.   van Dijk LV, Van den Bosch L, Aljabar P, Peressutti D, Both S, JHM Steenbakkers R, Langendijk JA, Gooding MJ, Brouwer CL. Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring. Radiother Oncol. 2020 Jan;142:115-123.

This content was created in conjunction with the Australian Society of Medical Imaging and Radiation Therapy (ASMIRT) CPD Program.
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