A1 Journal article – refereed

A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study




List of Authors: Kiljunen T, Akram S, Niemelä J, Löyttyniemi E, Seppälä J, Heikkilä J, Vuolukka K, Kääriäinen OS, Heikkilä VP, Lehtiö K, Nikkinen J, Gershkevitsh E, Borkvel A, Adamson M, Zolotuhhin D, Kolk K, Pang EPP, Tuan JKL, Master Z, Chua MLK, Joensuu T, Kononen J, Myllykangas M, Riener M, Mokka M, Keyriläinen J

Publisher: MDPI

Publication year: 2020

Journal: Diagnostics

Journal name in source: DIAGNOSTICS

Journal acronym: DIAGNOSTICS

Volume number: 10

Issue number: 11

Number of pages: 11

eISSN: 2075-4418

DOI: http://dx.doi.org/10.3390/diagnostics10110959


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.

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Last updated on 2021-24-06 at 08:46