The utility of atlas-assisted segmentation in the male pelvis is dependent on the interobserver agreement of the structures segmented

Langmack, K A; Perry, Chava; Sinstead, C; Mills, John A.; Saunders, Daniel

doi:10.1259/bjr.20140299

Cited by 14 publications

(28 citation statements)

References 20 publications

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“…The DL-based AST evaluated in this study could provide a basis for improving the accuracy and consistency of the most critical step in radiation delivery to control disease and limit adverse effects related to errors in the delineation of structures in RTP of prostate cancer patients. Langmack et al found that the DSC should be larger than 0.65 to save time using AST and time saving increases linearly with the DSC [27]. The mean DSC per patient in all clinics was higher than 0.60 and only in two scans below 0.65, indicating there should have been reasonable time saved in all clinics.…”

Section: Discussionmentioning

confidence: 95%

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

Kiljunen

Akram²,

Niemelä³

et al. 2020

Diagnostics

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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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Section: Discussionmentioning

confidence: 95%

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

Kiljunen

Akram²,

Niemelä³

et al. 2020

Diagnostics

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“…The total segmentation time including the manual operator interaction time was less than 1.5 min per 3D image which is substantially less than fully manual delineation time of 4.46 min reported in Ref. .…”

Section: Discussionmentioning

confidence: 65%

“…PCa radiation therapy planning is performed with the prostate border delineated on computed tomography (CT) images . However, due to the low soft tissue contrast between the prostate and surrounding tissues, manual contouring of the prostate in CT images is time consuming and is subject to high intraobserver and interobserver variability . Therefore, computer‐assisted segmentation algorithms are being investigated and developed to perform prostate contouring more quickly and more reproducibly compared to manual segmentation.…”

Section: Introductionmentioning

confidence: 99%

A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling

et al. 2018

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The proposed, semiautomatic segmentation algorithm showed a fast, robust, and accurate performance for 3D prostate segmentation of CT images, specifically when no previous, intrapatient information, that is, previously segmented images, was available. The accuracy of the algorithm is comparable to the best performance results reported in the literature and approaches the interexpert variability observed in manual segmentation.

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“…The results are comparable to the previously published work in the literature with a higher average sensitivity rate. The average 3D segmentation time, including the manual operator interaction time, is 75 seconds per image which is more than three times lower than manual prostate segmentation time reported in [21]. Since the presented technique is not related to previously acquired, and/or segmented CT images from the same target patient, it could be used for radiation therapy planning CT image segmentation.…”

Section: Discussionmentioning

confidence: 86%

A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics

Shahedi

Halicek

et al. 2018

Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

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Prostate segmentation in computed tomography (CT) images is useful for planning and guidance of the diagnostic and therapeutic procedures. However, the low soft-tissue contrast of CT images makes the manual prostate segmentation a time-consuming task with high inter-observer variation. We developed a semi-automatic, three-dimensional (3D) prostate segmentation algorithm using shape and texture analysis and have evaluated the method against manual reference segmentations. In a training data set we defined an inter-subject correspondence between surface points in the spherical coordinate system. We applied this correspondence to model the globular and smoothly curved shape of the prostate with 86, well-distributed surface points using a point distribution model that captures prostate shape variation. We also studied the local texture difference between prostate and non-prostate tissues close to the prostate surface. For segmentation, we used the learned shape and texture characteristics of the prostate in CT images and we used a set of user inputs for prostate localization. We trained our algorithm using 23 CT images and tested it on 10 images. We evaluated the results compared with those of two experts’ manual reference segmentations using different error metrics. The average measured Dice similarity coefficient (DSC) and mean absolute distance (MAD) were 88 ± 2% and 1.9 ± 0.5 mm, respectively. The averaged inter-expert difference measured on the same dataset was 91 ± 4% (DSC) and 1.3 ± 0.6 mm (MAD). With no prior intra-patient information, the proposed algorithm showed a fast, robust and accurate performance for 3D CT segmentation.

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The utility of atlas-assisted segmentation in the male pelvis is dependent on the interobserver agreement of the structures segmented

Cited by 14 publications

References 20 publications

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

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

A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling

A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics

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