Postediting prostate magnetic resonance imaging segmentation consistency and operator time using manual and computer-assisted segmentation: multiobserver study

Abstract

Prostate segmentation on T2w MRI is important for several diagnostic and therapeutic procedures for prostate cancer. Manual segmentation is time-consuming, labor-intensive, and subject to high interobserver variability. This study investigated the suitability of computer-assisted segmentation algorithms for clinical translation, based on measurements of interoperator variability and measurements of the editing time required to yield clinically acceptable segmentations. A multioperator pilot study was performed under three pre- and postediting conditions: manual, semiautomatic, and automatic segmentation. We recorded the required editing time for each segmentation and measured the editing magnitude based on five different spatial metrics. We recorded average editing times of 213, 328, and 393 s for manual, semiautomatic, and automatic segmentation respectively, while an average fully manual segmentation time of 564 s was recorded. The reduced measured postediting interoperator variability of semiautomatic and automatic segmentations compared to the manual approach indicates the potential of computer-assisted segmentation for generating a clinically acceptable segmentation faster with higher consistency. The lack of strong correlation between editing time and the values of typically used error metrics ([Formula: see text]) implies that the necessary postsegmentation editing time needs to be measured directly in order to evaluate an algorithm's suitability for clinical translation.

A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling

“…The average operator interaction time was 27 s. per subject. The total segmentation time including the operator interaction time was about one min per 3D image which is substantially less than fully manual segmentation time reported in the literature (i.e., about 5 to 20 minutes per 3D image) [6,19,35].…”

“…Contouring of the prostate gland on T2-weighted MR images could assist with MRI-guided procedures used for PCa diagnosis and management [4]. However, manual delineation of the prostate border is a time-consuming task with high inter-observer variability [5,6]. One solution to address these issues is to use computer-assisted segmentation algorithms.…”

“…There have also been some studies published for validation of the segmentation algorithms designed for prostate MRI [6,18,19]. Martin et al [19] have presented a three-phase study to validate prostate segmentation on MR images.…”

“…They reported up to 49% relative timesaving for using automatic or semiautomatic segmentation compared to manual segmentation. Shahedi et al [6] have conducted a multi-observer user study to investigate the prostate MRI segmentation repeatability and operation time after manual editing. They measured the required editing time to yield clinically acceptable MRI prostate segmentation and the inter-observer variability of computer-assisted segmentation after manual editing.…”

Incorporating minimal user input into deep learning based image segmentation

“…Therefore, an observer study is required to confirm the effectiveness of this approach for prostate segmentation in a real clinical situation. In addition, although our previous studies 13,14,17 showed that using minimal user interaction for prostate segmentation could substantially speed up the process when compared to manual segmentation time, the observer study must confirm that for this study.…”