University of Oulu

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.

A deep learning-based automated CT segmentation of prostate cancer anatomy for radiation therapy planning : a retrospective multicenter study

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Author: Kiljunen, Timo1; Akram, Saad2; Niemelä, Jarkko2;
Organizations: 1Docrates Cancer Center, Saukonpaadenranta 2, FI-00180 Helsinki, Finland
2MVision Ai, c/o Terkko Health hub, Haartmaninkatu 4, FI-00290 Helsinki, Finland
3Department of Biostatistics, University of Turku, Kiinamyllynkatu 10, FI-20014 Turku, Finland
4Kuopio University Hospital, Center of Oncology, Kelkkailijantie 7, FI-70210 Kuopio, Finland
5Oulu University Hospital, Department of Oncology and Radiotherapy, Kajaanintie 50, FI-90220 Oulu, Finland
6University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Aapistie 5 A, FI-90220 Oulu, Finland
7North Estonia Medical Centre, J. Sütiste tee 19, 13419 Tallinn, Estonia
8National Cancer Centre Singapore, Division of Radiation Oncology, 11 Hospital Crescent, Singapore 169610, Singapore
9Oncology Academic Programme, Duke-NUS Medical School, Singapore 169857, Singapore
10National Cancer Centre Singapore, Division of Medical Sciences, Singapore 169610, Singapore
11Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland
12Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2020
Publish Date: 2021-02-15


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.

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Series: Diagnostics
ISSN: 2075-4418
ISSN-E: 2075-4418
ISSN-L: 2075-4418
Volume: 10
Issue: 11
Article number: 959
DOI: 10.3390/diagnostics10110959
Type of Publication: A1 Journal article – refereed
Field of Science: 3126 Surgery, anesthesiology, intensive care, radiology
3122 Cancers
Copyright information: © 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 (