A deep learning-based automated CT segmentation of prostate cancer anatomy for radiation therapy planning : a retrospective multicenter study
Kiljunen, Timo; Akram, Saad; Niemelä, Jarkko; Löyttyniemi, Eliisa; Seppälä, Jan; Heikkilä, Janne; Vuolukka, Kristiina; Kääriäinen, Okko-Sakari; Heikkilä, Vesa-Pekka; Lehtiö, Kaisa; Nikkinen, Juha; Gershkevitsh, Eduard; Borkvel, Anni; Adamson, Merve; Zolotuhhin, Daniil; Kolk, Kati; Pang, Eric Pei Ping; Tuan, Jeffrey Kit Loong; Master, Zubin; Chua, Melvin Lee Kiang; Joensuu, Timo; Kononen, Juha; Myllykangas, Mikko; Riener, Maigo; Mokka, Miia; Keyriläinen, Jani (2020-11-17)
Kiljunen, T.; Akram, S.; Niemelä, J.; Löyttyniemi, E.; Seppälä, J.; Heikkilä, J.; Vuolukka, K.; Kääriäinen, O.-S.; Heikkilä, V.-P.; Lehtiö, K.; Nikkinen, J.; Gershkevitsh, E.; Borkvel, A.; Adamson, M.; Zolotuhhin, D.; Kolk, K.; Pang, E.P.P.; Tuan, J.K.L.; Master, Z.; Chua, M.L.K.; Joensuu, T.; Kononen, J.; Myllykangas, M.; Riener, M.; Mokka, M.; Keyriläinen, J. A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study. Diagnostics 2020, 10, 959. https://doi.org/10.3390/diagnostics10110959
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe202102154874
Tiivistelmä
Abstract
A commercial deep learning (DL)-based automated segmentation tool (AST) for computed tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer patients. Thirty patients from six clinics were reviewed with manual- (MC), automated- (AC) and automated and edited (AEC) contouring methods. In the AEC group, created contours (prostate, seminal vesicles, bladder, rectum, femoral heads and penile bulb) were edited, whereas the MC group included empty datasets for MC. In one clinic, lymph node CTV delineations were evaluated for interobserver variability. Compared to MC, the mean time saved using the AST was 12 min for the whole data set (46%) and 12 min for the lymph node CTV (60%), respectively. The delineation consistency between MC and AEC groups according to the Dice similarity coefficient (DSC) improved from 0.78 to 0.94 for the whole data set and from 0.76 to 0.91 for the lymph nodes. The mean DSCs between MC and AC for all six clinics were 0.82 for prostate, 0.72 for seminal vesicles, 0.93 for bladder, 0.84 for rectum, 0.69 for femoral heads and 0.51 for penile bulb. This study proves that using a general DL-based AST for CT images saves time and improves consistency.
Kokoelmat
- Avoin saatavuus [32007]