Development and evaluation of an automatic interstitial catheter digitization tool for adaptive high-dose-rate brachytherapy

Dise, Joseph; Lü, Xing; Scheuermann, Joshua; Anamalayil, Shibu; Mesina, Carmen F.; Lin, Lilie L.; Teo, Boon K.

doi:10.1016/j.brachy.2015.05.004

Cited by 17 publications

(13 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The geometric agreement reported in this article is consistent with prior work by Zhou et al, who developed a rigid registration method for a gynecological cylinder and Dise et al, who developed a region-growing algorithm for plastic interstitial needles (5,8). Zhou et al reported a disagreement of 0.75 AE 0.39 mm in a nine-patient study where the CT images had typical voxel dimensions of 1.17 Â 1.17 Â 2.0 mm 3 (8).…”

Section: Discussionsupporting

confidence: 90%

Automated applicator digitization for high-dose-rate cervix brachytherapy using image thresholding and density-based clustering

et al. 2020

View full text Add to dashboard Cite

The purpose of the study was to develop and evaluate an automated digitization algorithm for high-dose-rate cervix brachytherapy, with the goal of reducing the duration of treatment planning, staff resources, variability, and potential for human error. METHODS: An automated digitization algorithm was developed and retrospectively evaluated using treatment planning data from 10 patients with cervix cancer who were treated with a titanium tandem and ovoids applicator set. Applicators were segmented, without human interaction, by thresholding CT images to isolate high-density voxels and assigning the voxels to applicator and nonapplicator structures using HDBSCAN, a density-based linkage clustering algorithm. The applicator contours were determined from the centroid of the clustered voxels on each image slice and written to a treatment plan file. Automated contours were evaluated against manual digitization using distance and dosimetric metrics. RESULTS: A close agreement between automatic and manual digitization was observed. The mean magnitude of contour disagreement for 10 patients equaled 0.3 mm. Hausdorff distances were #1.0 mm. The applicator tip coordinates had submillimeter agreement. The median and mean dose volume histogram parameter differences were less than or equal to 1% for high-risk clinical target volume D 90 , high-risk clinical target volume D 95 , bladder D 2cc , rectum D 2cc , large bowel D 2cc , and small bowel D 2cc. The average execution time for the automated algorithm was less than 30 s. CONCLUSION: The digitization of titanium tandem and ovoids applicators for high-dose-rate brachytherapy treatment planning can be automated using straightforward thresholding and clustering algorithms. The adoption of automated digitization is expected to improve the consistency of treatment plans and reduce the duration of treatment planning.

show abstract

Section: Discussionsupporting

confidence: 90%

Automated applicator digitization for high-dose-rate cervix brachytherapy using image thresholding and density-based clustering

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The results in our study are similar to those from other studies using CNNs with 3D U-net architecture for needle segmentation in prostate brachytherapy in ultrasound images. 18,26,33,34 Wang et al 33 used a large training set and reported an RMSD of 0.74 mm, which is slightly higher than the 0.55 mm found in the present study. Zhang et al 18,34 and Dise et al 26 both reported lower RMSD (0.29 and 0.40 mm) than achieved in this study, but neither were able to reach the 100% sensitivity which is the case for the present study.…”

Section: Discussioncontrasting

confidence: 74%

“…Zhang et al 18,34 . and Dise et al 26 . both reported lower RMSD (0.29 and 0.40 mm) than achieved in this study, but neither were able to reach the 100% sensitivity which is the case for the present study.…”

Section: Discussioncontrasting

confidence: 59%

“…Deep learning in brachytherapy is a rapidly growing area of research. Previous studies include work on digitizing gynaecological applicators, 6,26,27 prostate seeds, 28,29 and segmentation of the prostate boundaries. [30][31][32] Recent publications by Zhang et al 18 , Wang et al 33 and Dise et al 26 have used deep learning for identifying needles in 3D TRUS images.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning‐based digitization of prostate brachytherapy needles in ultrasound images

et al. 2020

View full text Add to dashboard Cite

To develop, and evaluate the performance of, a deep learning-based three-dimensional (3D) convolutional neural network (CNN) artificial intelligence (AI) algorithm aimed at finding needles in ultrasound images used in prostate brachytherapy. Methods: Transrectal ultrasound (TRUS) image volumes from 1102 treatments were used to create a clinical ground truth (CGT) including 24422 individual needles that had been manually digitized by medical physicists during brachytherapy procedures. A 3D CNN U-net with 128 × 128 × 128 TRUS image volumes as input was trained using 17215 needle examples. Predictions of voxels constituting a needle were combined to yield a 3D linear function describing the localization of each needle in a TRUS volume. Manual and AI digitizations were compared in terms of the root-mean-square distance (RMSD) along each needle, expressed as median and interquartile range (IQR). The method was evaluated on a data set including 7207 needle examples. A subgroup of the evaluation data set (n = 188) was created, where the needles were digitized once more by a medical physicist (G1) trained in brachytherapy. The digitization procedure was timed. Results: The RMSD between the AI and CGT was 0.55 (IQR: 0.35-0.86) mm. In the smaller subset, the RMSD between AI and CGT was similar (0.52 [IQR: 0.33-0.79] mm) but significantly smaller (P < 0.001) than the difference of 0.75 (IQR: 0.49-1.20) mm between AI and G1. The difference between CGT and G1 was 0.80 (IQR: 0.48-1.18) mm, implying that the AI performed as well as the CGT in relation to G1. The mean time needed for human digitization was 10 min 11 sec, while the time needed for the AI was negligible. Conclusions: A 3D CNN can be trained to identify needles in TRUS images. The performance of the network was similar to that of a medical physicist trained in brachytherapy. Incorporating a CNN for needle identification can shorten brachytherapy treatment procedures substantially.

show abstract

“…In traditional approaches, like blood vessels and nerves, catheters are considered as tubular tree‐like structures 21,22 . These methods generally can be used in different imaging modalities including ultrasound (US), 19 CT, 20,23 and MRI 19 . Zhao et al 20 presented an automatic catheter tracking method in 3D US images by firstly applying a line filter method to enhance the contrast of the catheter against the background for regions‐of‐interest (ROI) selection using tubularness information, and then incorporating the Kalman filter into a catheter fitting model with the random sample consensus algorithm which was originally developed by Uherčík et al 19 to detect the catheters within ROIs.…”

Section: Introductionmentioning

confidence: 99%

Automatic multi‐catheter detection using deeply supervised convolutional neural network in MRI‐guided HDR prostate brachytherapy

et al. 2020

View full text Add to dashboard Cite

High-dose-rate (HDR) brachytherapy is an established technique to be used as monotherapy option or focal boost in conjunction with external beam radiation therapy (EBRT) for treating prostate cancer. Radiation source path reconstruction is a critical procedure in HDR treatment planning. Manually identifying the source path is labor intensive and time inefficient. In recent years, magnetic resonance imaging (MRI) has become a valuable imaging modality for image-guided HDR prostate brachytherapy due to its superb soft-tissue contrast for target delineation and normal tissue contouring. The purpose of this study is to investigate a deep-learning-based method to automatically reconstruct multiple catheters in MRI for prostate cancer HDR brachytherapy treatment planning. Methods: Attention gated U-Net incorporated with total variation (TV) regularization model was developed for multi-catheter segmentation in MRI. The attention gates were used to improve the accuracy of identifying small catheter points, while TV regularization was adopted to encode the natural spatial continuity of catheters into the model. The model was trained using the binary catheter annotation images offered by experienced physicists as ground truth paired with original MRI images. After the network was trained, MR images of a new prostate cancer patient receiving HDR brachytherapy were fed into the model to predict the locations and shapes of all the catheters. Quantitative assessments of our proposed method were based on catheter shaft and tip errors compared to the ground truth. Results: Our method detected 299 catheters from 20 patients receiving HDR prostate brachytherapy with a catheter tip error of 0.37 AE 1.68 mm and a catheter shaft error of 0.93 AE 0.50 mm. For detection of catheter tips, our method resulted in 87% of the catheter tips within an error of less than AE 2.0 mm, and more than 71% of the tips can be localized within an absolute error of no >1.0 mm. For catheter shaft localization, 97% of catheters were detected with an error of <2.0 mm, while 63% were within 1.0 mm. Conclusions: In this study, we proposed a novel multi-catheter detection method to precisely localize the tips and shafts of catheters in three-dimensional MRI images of HDR prostate brachytherapy. It paves the way for elevating the quality and outcome of MRI-guided HDR prostate brachytherapy.

show abstract

Development and evaluation of an automatic interstitial catheter digitization tool for adaptive high-dose-rate brachytherapy

Cited by 17 publications

References 10 publications

Automated applicator digitization for high-dose-rate cervix brachytherapy using image thresholding and density-based clustering

Automated applicator digitization for high-dose-rate cervix brachytherapy using image thresholding and density-based clustering

Deep learning‐based digitization of prostate brachytherapy needles in ultrasound images

Automatic multi‐catheter detection using deeply supervised convolutional neural network in MRI‐guided HDR prostate brachytherapy

Contact Info

Product

Resources

About