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

Automating implant reconstruction in interstitial brachytherapy of the breast: A hybrid approach combining electromagnetic tracking and image segmentation

BACKGROUND AND PURPOSE

To develop a method for automatic reconstruction of catheter implants in interstitial brachytherapy (iBT) of the breast by means of electromagnetic tracking (EMT) with the goal of making treatment planning as time-effective and accurate as possible.

MATERIALS AND METHODS

The implant geometry of 64 patients was recorded using an afterloader prototype with EMT functionality immediately after the planning CT. EMT data were transferred to the CT image space by rigidly registering the catheter fixation buttons as landmarks. To further improve reconstruction accuracy, the EMT reconstruction points were used as starting points to define small regions of interest (ROI) in the CT image. Within these ROIs, the catheter track was segmented in the CT using image processing operations such as thresholding and blob detection, thus refining the reconstruction. The perpendicular distance between the refined EMT implant reconstruction points and the manually reconstructed catheters by an experienced treatment planner was calculated as a measure of their geometric agreement.

RESULTS

Geometrically, the refined EMT based implant reconstruction shows excellent agreement with the manual reconstruction. The median distance across all patients is 0.25 mm and the 95th percentile is 1 mm. Refinement takes approximately 0.5 s per reconstruction point and typically does not exceed 3 min per implant at no user interaction.

CONCLUSION

The refined EMT based implant reconstruction proved to be extremely accurate and fast compared to manual reconstruction. The presented procedure can in principle be easily transferred to clinical routine and therefore has enormous potential to provide significant time savings in iBT treatment planning whilst improving reconstruction accuracy.

Deep-learning-assisted algorithm for catheter reconstruction during MR-only gynecological interstitial brachytherapy

Magnetic resonance imaging (MRI) offers excellent soft‐tissue contrast enabling the contouring of targets and organs at risk during gynecological interstitial brachytherapy procedure. Despite its advantage, one of the main obstacles preventing a transition to an MRI‐only workflow is that implanted plastic catheters are not reliably visualized on MR images. This study aims to evaluate the feasibility of a deep‐learning‐based algorithm for semiautomatic reconstruction of interstitial catheters during an MR‐only workflow. MR images of 20 gynecological patients were used in this study. Note that 360 catheters were reconstructed using T1‐ and T2‐weighted images by five experienced brachytherapy planners. The mean of the five reconstructed paths were used for training (257 catheters), validation (15 catheters), and testing/evaluation (88 catheters). To automatically identify and localize the catheters, a two‐dimensional (2D) U‐net algorithm was used to find their approximate location in each image slice. Once localized, thresholding was applied to those regions to find the extrema, as catheters appear as bright and dark regions in T1‐ and T2‐weighted images, respectively. The localized dwell positions of the proposed algorithm were compared to the ground truth reconstruction. Reconstruction time was also evaluated. A total of 34 009 catheter dwell positions were evaluated between the algorithm and all planners to estimate the reconstruction variability. The average variation was 0.97 ± 0.66 mm. The average reconstruction time for this approach was 11 ± 1 min, compared with 46 ± 10 min for the expert planners. This study suggests that the proposed deep learning, MR‐based framework has potential to replace the conventional manual catheter reconstruction. The adoption of this approach in the brachytherapy workflow is expected to improve treatment efficiency while reducing planning time, resources, and human errors.

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

PURPOSE

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.

A semiautomatic segmentation method for interstitial needles in intraoperative 3D transvaginal ultrasound images for high-dose-rate gynecologic brachytherapy of vaginal tumors

PURPOSE

The purpose of this study was to evaluate the use of a semiautomatic algorithm to simultaneously segment multiple high-dose-rate (HDR) gynecologic interstitial brachytherapy (ISBT) needles in three-dimensional (3D) transvaginal ultrasound (TVUS) images, with the aim of providing a clinically useful tool for intraoperative implant assessment.

METHODS AND MATERIALS

A needle segmentation algorithm previously developed for HDR prostate brachytherapy was adapted and extended to 3D TVUS images from gynecologic ISBT patients with vaginal tumors. Two patients were used for refining/validating the modified algorithm and five patients (8-12 needles/patient) were reserved as an unseen test data set. The images were filtered to enhance needle edges, using intensity peaks to generate feature points, and leveraged the randomized 3D Hough transform to identify candidate needle trajectories. Algorithmic segmentations were compared against manual segmentations and calculated dwell positions were evaluated.

RESULTS

All 50 test data set needles were successfully segmented with 96% of algorithmically segmented needles having angular differences <3° compared with manually segmented needles and the maximum Euclidean distance was <2.1 mm. The median distance between corresponding dwell positions was 0.77 mm with 86% of needles having maximum differences <3 mm. The mean segmentation time using the algorithm was <30 s/patient.

CONCLUSIONS

We successfully segmented multiple needles simultaneously in intraoperative 3D TVUS images from gynecologic HDR-ISBT patients with vaginal tumors and demonstrated the robustness of the algorithmic approach to image artifacts. This method provided accurate segmentations within a clinically efficient timeframe, providing the potential to be translated into intraoperative clinical use for implant assessment.

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

PURPOSE

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 ± 1.68 mm and a catheter shaft error of 0.93 ± 0.50 mm. For detection of catheter tips, our method resulted in 87% of the catheter tips within an error of less than ± 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.

Interstitial High-Dose-Rate Gynecologic Brachytherapy: Clinical Workflow Experience From Three Academic Institutions

An interstitial brachytherapy approach for gynecologic cancers is typically considered for patients with lesions exceeding 5 mm within tissue or that are not easily accessible for intracavitary applications. Recommendations for treating gynecologic malignancies with this approach are available through the American Brachytherapy Society, but vary based on available resources, staffing, and logistics. The intent of this manuscript is to share the collective experience of 3 academic centers that routinely perform interstitial gynecologic brachytherapy. Discussion points include indications for interstitial implants, procedural preparations, applicator selection, anesthetic options, imaging, treatment planning objectives, clinical workflows, timelines, safety, and potential challenges. Interstitial brachytherapy is a complex, high-skill procedure requiring routine practice to optimize patient safety and treatment efficacy. Clinics planning to implement this approach into their brachytherapy practice may benefit from considering the discussion points shared in this manuscript.

Deep-learning assisted automatic digitization of interstitial needles in 3D CT image based high dose-rate brachytherapy of gynecological cancer

Digitization of interstitial needles is a complicated and tedious process for the treatment planning of 3D CT image based interstitial high dose-rate brachytherapy (HDRBT) of gynecological cancer. We developed a deep-learning assisted auto-digitization method for interstitial needles. The digitization method consisted of two steps. The first step used a deep neural network with a U-net structure to segment all needles from CT images. The second step simultaneously clustered the segmented voxels into different needle groups and generated the needle central trajectories by solving an optimization problem. We evaluated the effectiveness of the developed method in ten interstitial HDRBT patient cases that were not used in the training of the U-net. Average number of needles per case was 20.7. For the segmentation step, average Dice similarity coefficient between automatic and manual segmentation was 0.93. For the digitization step, Hausdorff distance between needle trajectories determined by our method and manually by qualified medical physicists was ~0.71 mm on average and mean difference of tip positions was ~0.63 mm, which were considered acceptable for HDRBT treatment planning. It took ~5 min to complete the digitization process of an interstitial HDRBT case. The achieved accuracy and efficiency made our method clinically attractive.

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

PURPOSE

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 D₉₀, high-risk clinical target volume D₉₅, 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.

Deep-learning-assisted automatic digitization of applicators in 3D CT image-based high-dose-rate brachytherapy of gynecological cancer

PURPOSE

Applicator digitization is one of the most critical steps in 3D high-dose-rate brachytherapy (HDRBT) treatment planning. Motivated by recent advances in deep-learning, we propose a deep-learning-assisted applicator digitization method for 3D CT image-based HDRBT. This study demonstrates its feasibility and potential in gynecological cancer HDRBT.

METHODS AND MATERIALS

Our method consisted of two steps. The first step used a U-net to segment applicator regions. We trained the U-net using two-dimensional CT images with a tandem-and-ovoid (T&O) applicator and corresponding applicator mask images. The second step applied a spectral clustering method and a polynomial curve fitting method to extract applicator central paths. We evaluated the accuracy, efficiency, and robustness of our method in different scenarios including other T&O cases that were not used in training, a T&O case scanned with cone-beam CT, and Y-tandem and cylinder-applicator cases.

RESULTS

In test cases with a T&O applicator, average 3D Dice similarity coefficient between automatic and manual segmented applicator regions was 0.93. Average distance between tip positions and average Hausdorff distance between applicator channels determined by our method and manually were 0.64 mm and 0.68 mm, respectively. Although trained only using CT images of T&O cases, our tool can also digitize Y-tandem, cylinder applicator, and T&O applicator scanned in cone-beam CT with error of tip position and Hausdorff distance <1 mm. Computation time was ∼15 s per case.

CONCLUSIONS

We have developed a deep-learning-assisted applicator digitization tool for 3D CT image-based HDRBT of gynecological cancer. The achieved accuracy, efficiency, and robustness made our tool clinically attractive.

Assessment of the implant geometry in fractionated interstitial HDR breast brachytherapy using an electromagnetic tracking system

PURPOSE

During the partial-breast treatment course by interstitial brachytherapy, electromagnetic tracking (EMT) was applied to measure the implant geometry. Implant-geometry variation, choice of reference data, and three registration methods were assessed.

METHODS AND MATERIALS

The implant geometry was measured in 28 patients after catheter implantation (EMT_bed), during CT imaging (EMT_CT), and in each of up to n = 9 treatment fractions (EMT_{F(k), k = 1, 2,… n}). EMT_F(k) were registered to the planned implant reconstruction (CT_plan) by using all dwell positions (DPs), the button centers, or three fiducial sensors on the patient's skin. Variation in implant geometry obtained from EMT_F(k) was assessed for EMT_bed, EMT_CT, and CT_plan.

RESULTS

EMT was used to measure 3932 catheters. A duration of 6.5 ± 1.7 min was needed for each implant measurement (mean, 17 catheters) plus setup of the EMT system. Data registration based on the DP deviated significantly lower than registration on button centers or fiducial sensors. Within a registration group, there was a <0.5-mm difference in the choice of reference data. Using CT_plan as reference for registration, the mean residual distance of DPs on EMT-derived DPs was found at 2.1 ± 1.6 mm (EMT_bed), 1.3 ± 0.9 mm (EMT_CT), and 2.5 ± 1.5 mm (EMT_F(k)).

CONCLUSIONS

EMT can assess the implant geometry in high-dose-rate interstitial brachytherapy breast treatments. EMT_bed, EMT_CT, and CT_plan data can serve as reference for assessment of implant changes.