Trimpl MJ., Campbell S., Panakis N., Ajzensztejn D., Burke E., Ellis S., Johnstone P., Doyle E., Towers R., Higgins G., Bernard C., Hustinx R., Vallis KA., Stride EPJ., Gooding MJ.

BACKGROUND AND PURPOSE: To evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring. MATERIALS AND METHODS: Nine clinicians contoured the gross tumour volume (GTV) using the PET-CT scans of 10 non-small cell lung cancer (NSCLC) patients, either using DL-assisted or manual contouring tools. After contouring a case using one contouring method, the same case was contoured one week later using the other method. The contours and time taken were compared. RESULTS: Use of the DL-assisted tool led to a statistically significant decrease in active contouring time of 23 % relative to the standard manual segmentation method (p 

Deep Learning-Assisted interactive contouring of lung Cancer: Impact on contouring time and consistency.

Trimpl MJ., Campbell S., Panakis N., Ajzensztejn D., Burke E., Ellis S., Johnstone P., Doyle E., Towers R., Higgins G., Bernard C., Hustinx R., Vallis KA., Stride EPJ., Gooding MJ.

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