eISSN: 2081-2841
ISSN: 1689-832X
Journal of Contemporary Brachytherapy
Current Issue Archive Supplements Articles in Press Journal Information Aims and Scope Editorial Office Editorial Board Register as Author Register as Reviewer Instructions for Authors Abstracting and indexing Subscription Advertising Information Links
Editorial System
Submit your Manuscript
SCImago Journal & Country Rank

2/2023
vol. 15
 
Share:
Share:
Original paper

Automatic reconstruction of interstitial needles using CT images in post-operative cervical cancer brachytherapy based on deep learning

Hongling Xie
1
,
Jiahao Wang
1
,
Yuanyuan Chen
1
,
Yeqiang Tu
1
,
Yukai Chen
1
,
Yadong Zhao
1
,
Pengfei Zhou
1
,
Shichun Wang
2
,
Zhixin Bai
2
,
Qiu Tang
1

  1. Department of Radiation Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
  2. Hangzhou Ruicare MedTech Co., Ltd., Hangzhou, Zhejiang, China
J Contemp Brachytherapy 2023; 15, 2: 134–140
Online publish date: 2023/04/06
Article file
Get citation
 
 

Purpose

Cervical cancer is the fourth most frequently diagnosed cancer, and the fourth leading cause of cancer-related deaths in women around the world [1]. External beam radiation therapy (EBRT) and brachytherapy (BT) are effective treatments for cervical cancer [2, 3]. The American Brachytherapy Society (ABS) recommends post-operative adjuvant BT for non-radical surgery, close or positive margins, large or deeply invasive tumors, and parametrial or vaginal involvement [4]. Several reports supported that vaginal cuff BT boost was associated with a reduced recurrence rate in post-operative setting of high-risk patients with early-stage cervical cancer [5, 6]. Currently, the application of 3D image-guided BT (IGBT) allows adaptive treatment planning process, and presents more advantageous, compared with conventional two-dimensional (2D), image-based method [7]. Meanwhile, applicator reconstruction is critical step during the procedure of IGBT treatment planning [8].

The accuracy of applicators reconstruction has significant impact on dosimetric results of IGBT treatment plan because of steep dose gradients [9-11]. In general, applicators reconstruction are mostly performed manually by physicists. The localization process is a time-consuming part in IGBT workflow [12], and always suffers from subjective variability. Therefore, it is strongly needed to achieve automatic applicator reconstruction in IGBT workflow to ensure treatment planning accuracy, consistency, and efficiency.

In recent years, deep learning (DL)-based frameworks have been applied in radiotherapy (RT) and achieved superior results, including automatic segmentation of target volume and organs at risk [13], automatic RT planning [14], and prediction of irradiation toxicity and prognosis [15]. The advantage of DL is the ability to recognize novel scenes by automatically extracting labeled features through learning of generalized features in training samples [16-18]. Deep learning plays a major role in brachytherapy [19]; some studies have focused on the automatic applicators reconstruction in IGBT workflow based on DL methods [20-27]. However, geometric metrics and subjective assessment were always selected to evaluate the performance of DL models in previous studies, with few studies reporting dosimetric differences in auto-reconstruction of applicators.

The purpose of the present study was to evaluate the accuracy of DL model in automatic reconstruction of metallic interstitial needles in patients with post-operative cervical cancer using both geometric and dosimetric metrics in IGBT workflow.

Material and methods

The work flowchart of this study is illustrated in Figure 1, and three key steps summarized the procedure. Firstly, the experienced physicist annotated metallic needles as the standard applicators reconstruction. Secondly, the DL-based model was trained and verified by the standard data, and automatic reconstructions of interstitial needles was generated. Thirdly, the accuracy of DL-based auto-reconstruction was assessed using geometric metrics and dosimetric differences.

Fig. 1

Flowchart of manual and deep learning (DL)-based auto-reconstruction evaluation experiment. Brachytherapy plans were designed and optimized based on standard and auto-reconstructed needles for dosimetric evaluation

/f/fulltexts/JCB/50520/JCB-15-50520-g001_min.jpg

Data annotation

Data of seventy post-operative cervical cancer patients collected between August, 2021 and July, 2022 were used in this study. In all patients, three interstitial needles were employed, with a prescription dose of 12-30 Gy (6 Gy/fraction). CT images of 70 patients were reconstructed with 512 × 512 matrix size and 3 mm slice thickness using a Philips Brilliance Big Bore CT scanner system (Philips Healthcare, Best, The Netherlands). The number of CT slices ranged from 69 to 118. Interstitial needles were delineated manually by an intern physicist using Oncentra treatment planning system, version 4.3 (Elekta AB, Stockholm, Sweden), and named 1, 2, and 3. To establish standard delineation, all the manual needles were evaluated and approved by a senior physicists.

Deep learning-based auto-reconstruction

We presented an adaptive DL model based on nnU-Net (no-new-Net) to reconstructed needles for post-operative cervical cancer BT. Figure 2 depicts the design concept for DL model used in the study. The nnU-Net [28] defines dataset fingerprint and pipeline fingerprint. Pipeline fingerprints are classified into three categories, including blueprint, inferred, and empirical parameters. A 2D U-Net, 3D U-Net, and 3D U-Net cascade are the three U-Net configurations that nnU-Net generates by default. 2D U-Net and 3D U-Net are input full resolution images, while 3D U-Net cascade firstly uses a low resolution image for coarse segmentation, and then applies a full resolution image for fine segmentation.

Fig. 2

Design of the nnU-Net

/f/fulltexts/JCB/50520/JCB-15-50520-g002_min.jpg

In this work, we mainly focused on the 3D U-Net for reconstruction of the applicator due to 3D-CT images with high resolution of metallic needles in our datasets. The 70 patients were separated into training, validating, and testing data in the ratio of 50 : 10 : 10. To improve image contrast and improve the interstitial needles display, histogram equalization processing on CT scans from training and validating data was performed using digital image processing software.

Geometric assessment

Geometric correctness of the interstitial needles was compared using DSC, 95% HD, and JC [29]. DSC and JC calculated spatial overlap between two regions as follows:

DSC = 2 |A ∩ B| / (|A| + |B|),

JC = |A ∩ B| / |A ∪ B|,

where A and B are manually segmented regions or auto-segmented regions based on DL. For the complete overlap, the values of DSC and JC are 1. For the incomplete overlap, the values of DSC and JC are close to 0.

Hausdorff distance (HD) was used to quantify the accuracy of digitized needle trajectories. In order to exclude outlier distance values, 95% HD was chosen to indicate the largest surface-to-surface separation among the closest 95% of surface points. Unit of HD95 was mm. The smaller the HD95, the better the segmentation. Hausdorff distance (HD) was computed as:

HD = max (h (A, B), h (B, A)),

With h defined as h (A, B) = max aA min bBd (a, b), where a and b are the points on the surfaces of A and B.

Dosimetric comparison

Oncentra treatment planning system was applied to compute and optimize BT plans (original plan), based on standard manual needles. Radio-active source position and time were migrated from the original plans to the automatic reconstruction needles to generate DL plans. Dose volume histogram (DVH) was used to investigate the dosimetric difference between original plans and DL plans. For high-risk clinical target volume (HR-CTV), we mainly focused on D90%, D98% (D98 and D90 were doses to 98% and 90% of HR-CTV volume, respectively). For organs at risk (OARs), we mainly focused on D2cc, D1cc, and D0.1cc. Dose values of D2cc, D1cc, and D0.1cc represented 2 cc, 1 cc and 0.1 cc volumes of OARs that received the maximum dose, respectively. OARs included the bladder, rectum, sigmoid colon, and small intestine.

Statistical analysis

IBM SPSS statistics software (version 26.0, IBM Inc., Armonk, NY, USA) was used for statistical analysis, where mean ± standard deviation (SD) was applied for presenting and summarizing the results. Wilcoxon’s paired non-parametric signed-rank test was used to compare the dosimetric difference between two methods, p < 0.05 indicated that the difference was statistically significant. Spearman’s correlation analysis was applied to assess the relationships between geometric metrics and dosimetric difference.

Results

Evaluation of geometric metrics

The geometric accuracy of DL auto-reconstruction of metallic needles is presented in Figure 3. Automatic reconstruction produced the results for three needles, with average DSC value of 0.88 ±0.03, 0.89 ±0.02, and 0.9 ±0.02, respectively; 95% HD of 0.77 ±0.12 mm, 0.73 ±0.13 mm, and 0.71 ±0.07 mm, respectively; and JC of 0.81 ±0.04, 0.8 ±0.34, and 0.81 ±0.03, respectively.

Fig. 3

Dice similarity coefficient, 95% Hausdorff distance, and Jaccard coefficient box plot from comparing deep learning (DL)-based automatic and manual reconstruction for three metallic needles

/f/fulltexts/JCB/50520/JCB-15-50520-g003_min.jpg

Evaluation of dosimetric metrics

Table 1 demonstrates the comparisons of dosimetric parameters between two methods using Wilcoxon’s paired non-parametric signed-rank test. There were no statistically significant dosimetric differences for all of the BT planning structures (p > 0.05). Figure 4 illustrates 3D views of three metallic needles for manual and automatic reconstructions. The reconstructions of applicators with DL model were in good agreement with the manual approach. Examples of dose distributions from manual and DL-based methods are shown in Figure 5.

Table 1

Dosimetric comparisons between two methods in brachytherapy treatment plans

StructureGeometric parametersManual delineationAutomatic delineationDifferences (%)ZP-value
Mean ± standard deviation
HR-CTVD90 (cGy)663.45 ±12.98661.03 ±12.680.36–0.2550.799
D98 (cGy)663.45 ±12.98562.69 ±17.930.63–0.5610.575
BladderD2cc (cGy)269.13 ±53.36269.92 ±53.13–0.29–0.5610.575
D1cc (cGy)299.32 ±59.24300.22 ±59.22–0.30–0.5610.575
D0.1cc (cGy)367.56 ±74.66368.35 ±75.23–0.21–0.4080.683
RectumD2cc (cGy)304.05 ±27.19299.83 ±29.111.39–1.6820.093
D1cc (cGy)350.75 ±33.94339.88 ±35.763.10–1.7840.074
D0.1cc (cGy)460.81 ±72.48437.44 ±66.075.07–1.7840.074
SigmoidD2cc (cGy)85.94 ±38.3185.60 ±37.980.40–0.7640.445
D1cc (cGy)100.69 ±53.76100.29 ±53.040.40–0.6630.508
D0.1cc (cGy)142.50 ±93.38141.44 ±91.040.74–0.9680.333
IntestineD2cc (cGy)81.11 ±54.8281.48 ±56.08–0.46–0.2550.799
D1cc (cGy)92.61 ±65.8093.42 ±68.24–0.87–0.0510.959
D0.1cc (cGy)124.29 ±94.70125.69 ±98.85–1.13–0.3380.735
Fig. 4

3D views of three metallic needles for manual and automatic reconstructions. The three white needles were manually reconstructed by a physicists. The red, blue and green needles were automatically reconstructed based on the deep learning (DL) model

/f/fulltexts/JCB/50520/JCB-15-50520-g004_min.jpg
Fig. 5

Examples of dose distributions from manual and deep learning (DL)-based methods. A) Dosimetric results with manual reconstruction of the needles. B) Dosimetric results with automatic reconstruction of the needles. Purple lines represent dose distributions with 100% (600 cGy) prescription. Red lines represent dose distributions with 150% (900 cGy) prescription. Blue in the picture represents HR-CTV

/f/fulltexts/JCB/50520/JCB-15-50520-g005_min.jpg

Correlation analysis between geometric and dosimetric metrics

The results of Spearman correlation analysis between geometric metrics and dosimetric metrics (Δdose) are presented in Table 2. The correlation analysis demonstrated weak link between all of the dosimetric difference and its geometric metrics in the BT planning structures.

Table 2

Correlations between geometric metrics and dosimetric differences

StructureGeometric parametersDSC95% HDJC
Rp-valueRp-valueRp-value
HR-CTVD90–0.1760.6270.4040.247–0.2390.507
D98–0.1850.6090.4290.216–0.0920.800
BladderD2cc–0.1220.7380.2640.460–0.2380.509
D1cc–0.1170.7480.2610.467–0.2300.523
D0.1cc–0.3000.3990.4080.242–0.4900.240
RectumD2cc–0.3020.3970.3610.305–0.0290.937
D1cc–0.0110.9760.2000.5800.0800.827
D0.1cc0.3850.2720.1540.6710.1240.732
SigmoidD2cc0.0410.910–0.1100.763–0.0820.822
D1cc–0.0600.870–0.0700.848–0.1870.606
D0.1cc–0.1460.688–0.0960.792–0.2400.504
IntestineD2cc–0.2340.5150.1310.719–0.3730.289
D1cc–0.2260.5300.0090.979–0.3110.382
D0.1cc–0.2600.4960.0480.894–0.3410.335

Discussion

Brachytherapy is the key aspect of treatment for post-operative cervical cancer with high-risk factors. 3D-IGBT technology can produce the optimal dose distribution in target regions, and decrease the radiation dose to healthy tissues [30]. However, IGBT procedures with increasing real-time steps require more technical and manpower resources. The applicator reconstruction is one of the real-time steps in the design of 3D-IGBT plan, and the reconstruction accuracy is always dependent on the experience and subjective assessment of physicists. This highlights the importance of a rapid and accurate reconstruction method, which would improve the IGBT workflow through automation. In this work, we proposed a DL-based model for automatic needle reconstruction during CT image interstitial IGBT treatment planning.

Various applicators are used for implantation before IGBT planning, such as intra-uterine and ovoid tubes, vaginal applicator, ring applicator, interstitial needle, etc. Using different methods or DL models would generate different reconstruction results for a specific applicator. We summarized auto-segmentation results for applicators in brachytherapy from other published literature. The comparison of DSC and HD for different methods is presented in Table 3. Image thresholding and density-based clustering were applied to segment the tandem and ovoids applicator, and HD was ≤ 1 mm [31]. A DSD-U-Net model [26] was proposed to reconstruct the intra-uterine and ovoid tubes, and achieved average DSC value of 0.92. A U-Net model [32] was used to automatically segment Fletcher applicator with average DSC value of 0.89. A 2D U-Net algorithm [33] was tested to reconstruct the needles, with average DSC value of 0.59 and HD value of 4.2 mm, based on MR images. Two phases DL-based segmentation and object-tracking algorithms were adopted to reconstruct the interstitial needles in CT-guided prostate brachytherapy. In a study [34], DSC between the network output and the ground truth was 0.95. In the present work, the nnU-Net model was trained and reconstructed the metallic needles with average DSC value of 0.89, and 95% HD value of 0.74 mm based on CT images. Compared with the type of tandem and ovoid applicator, the type of needle applicator is more difficult for location due to its slender shape (about 2 cc) in 3D images. Peroni [35] reported a range of DSC values that generally denoted good agreement depending on structure volume, such as the agreement of DSC value of 0.4-0.6, when the structure volume is 1-5 cc. Evidently, our DL model obtained superior geometric accuracy of needles reconstruction. This mainly benefits form the cross-validation of nnU-Net, and achieves the best ensemble during training process.

Table 3

Summary of auto-segmentation results for applicators in brachytherapy from other published literature

Author(s) [Ref.]Methods or DL modelsImage typeApplicatorsEnrolled patientsResults
Deufel et al. [31]Image thresholding and density-basedCTTandem and ovoid10 patients from Mayo Clinic for testingDSC not used;
HD ≤ 1 mm
Zhang et al. [26]DSD-U-NetCTTandem and ovoid91 cases from Tianjin Medical University Cancer Institute and Hospital; 32 internal cases for testingDSC: 0.92; HD: 2.3 mm
Hu et al. [32]U-NetCTFletcher60 cases from Sichuan Cancer Hospital;
10 independent cases for testing
DSC: 0.89; HD: 1.66 mm
Shaaer et al. [33]2D U-NetMRIInterstitial plastic needles20 cases from Odette Cancer Centre;
Odette Cancer Centre
DSC: 0.59; HD: 0.42 mm
Mohammad Mahdi et al. [34]Two-phase DL modelsCTInterstitial plastic needles25 cases from Shohada-eTajrish Educational Hospital;
5 internal cases for testing
DSC: 0.95; HD not used
Our methodnnU-Net (3D)CTInterstitial metal needles70 cases from Women’s Hospital in China;
10 internal cases for testing
DSC: 0.89; HD: 0.74 mm

Dosimetric evaluation is necessary for automatic reconstruction in IGBT workflow. Yoganathan et al. [36] demonstrated the importance of dosimetric evaluation over geometric evaluation for an automatic problem in cervical cancer BT. Schindel et al. [37] reported that the reconstruction uncertainty could cause dosimetry change greater than 10% for MRI-based BT. Therefore, we compared the dose distribution between standard original BT plan and DL plan for every planning structure. Wilcoxon signed-rank test indicated no significant dosimetric differences in HR-CTV and OARs between the two methods. Meanwhile, Spearman correlation analysis showed weak link between geometric metrics and dosimetric differences. This might prove the automatic reconstructions of metallic needles are an alternative to the manual operation.

In this work, we investigated the performance of DL-based automatic reconstruction of metal needles in post-operative cervical cancer patients treated with IGBT. Furthermore, automatic method would improve the accuracy and efficiency, and decrease the uncertainties in adaptive IGBT process. Moreover, the application of intelligent methods may promote the development of BT, and auto-reconstruction of applicator is one of the essential tasks in the component of fully automatic IGBT plan.

There are still several limitations in this study. First, this auto-reconstruction approach may not be suitable for other situations, such as vaginal applicator or Fletcher applicator. The reason was mainly caused by single-training dataset in our DL-based model, and increasing the amount of training data including various applicators in IGBT workflow could make the DL model more robust. Second, the model was developed and evaluated based on CT images. The ability of DL-based model lacks evaluation of other imaging modalities. For different imaging settings, re-training of the DL model is recommended to ensure similar performance.

Conclusions

This study has demonstrated that our DL-based reconstruction method can be used to precisely localize metal interstitial needles in post-operative cervical cancer IGBT with 3D-CT images. The proposed automatic approach can reduce the variability and relieve physicists from the labor-intensive tasks.

Disclosure

The authors report no conflict of interest.

References

1 

Sung H, Ferlay J, Siegel RL et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71: 209-249.

2 

Prescribing, recording, and reporting brachytherapy for cancer of the cervix. J ICRU 2013; 13: NP.

3 

Tanderup K, Eifel PJ, Yashar CM et al. Curative radiation therapy for locally advanced cervical cancer: brachytherapy is NOT optional. Int J Radiat Oncol Biol Phys 2014; 88: 537-539.

4 

Lai YL, Jin YN, Wang X et al. The case selection for vaginal cuff brachytherapy in cervical cancer patients after radical hysterectomy and external beam radiation therapy. Front Oncol 2021; 11: 685972.

5 

Li L, Kou X, Feng X et al. Postoperative external beam irradiation with and without brachytherapy in pelvic node-positive IB1-IIA2 cervical cancer patients: a retrospective clinical study. Radiat Oncol 2015; 10: 189.

6 

Mauro GP, Kleine RT, da Costa SCS et al. Vaginal cuff brachytherapy in the adjuvant setting for patients with high-risk early-stage cervical cancer. Brachytherapy 2019; 18: 747-752.

7 

Suzumura EA, Gama LM, Jahn B et al. Effects of 3D image-guided brachytherapy compared to 2D conventional brachytherapy on clinical outcomes in patients with cervical cancer: A systematic review and meta-analyses. Brachytherapy 2021; 20: 710-737.

8 

Hellebust TP, Kirisits C, Berger D et al. Recommendations from Gynaecological (GYN) GEC-ESTRO Working Group: considerations and pitfalls in commissioning and applicator reconstruction in 3D image-based treatment planning of cervix cancer brachytherapy. Radiother Oncol 2010; 96: 153-160.

9 

Tanderup K, Hellebust TP, Lang S et al. Consequences of random and systematic reconstruction uncertainties in 3D image based brachytherapy in cervical cancer. Radiother Oncol 2008; 89: 156-163.

10 

Kim Y, Muruganandham M, Modrick JM et al. Evaluation of artifacts and distortions of titanium applicators on 3.0-Tesla MRI: feasibility of titanium applicators in MRI-guided brachytherapy for gynecological cancer. Int J Radiat Oncol Biol Phys 2011; 80: 947-955.

11 

Wu A, Tang D, Wu A et al. Comparison of the dosimetric influence of applicator displacement on 2D and 3D brachytherapy for cervical cancer treatment. Technol Cancer Res Treat 2021; 20: 15330338211041201.

12 

Mayadev J, Qi L, Lentz S et al. Implant time and process efficiency for CT-guided high-dose-rate brachytherapy for cervical cancer. Brachytherapy 2014; 13: 233-239.

13 

Jiang X, Wang F, Chen Y et al. RefineNet-based automatic delineation of the clinical target volume and organs at risk for three-dimensional brachytherapy for cervical cancer. Ann Transl Med 2021; 9: 1721.

14 

Shen C, Chen L, Gonzalez Y, Jia X. Improving efficiency of training a virtual treatment planner network via knowledge-guided deep reinforcement learning for intelligent automatic treatment planning of radiotherapy. Med Phys 2021; 48: 1909-1920.

15 

Deist TM, Dankers F, Valdes G et al. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers. Med Phys 2018; 45: 3449-3459.

16 

Larentzakis A, Lygeros N. Artificial intelligence (AI) in medicine as a strategic valuable tool. Pan Afr Med J 2021; 38: 184.

17 

Connor CW. Artificial intelligence and machine learning in anesthesiology. Anesthesiology 2019; 131: 1346-1359.

18 

Gupta R, Srivastava D, Sahu M et al. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25: 1315-1360.

19 

Fechter T, Sachpazidis I, Baltas D. The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data. Z Med Phys 2022; S0939-3889(22)00099-X.

20 

Jung H, Gonzalez Y, Shen C et al. Deep-learning-assisted automatic digitization of applicators in 3D CT image-based high-dose-rate brachytherapy of gynecological cancer. Brachytherapy 2019; 18: 841-851.

21 

Jung H, Shen C, Gonzalez Y et al. Deep-learning assisted automatic digitization of interstitial needles in 3D CT image based high dose-rate brachytherapy of gynecological cancer. Phys Med Biol 2019; 64: 215003.

22 

Shen C, Gonzalez Y, Klages P et al. Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer. Phys Med Biol 2019; 64: 115013.

23 

Dai X, Lei Y, Zhang Y et al. Automatic multi-catheter detection using deeply supervised convolutional neural network in MRI-guided HDR prostate brachytherapy. Med Phys 2020; 47: 4115-4124.

24 

Gillies DJ, Rodgers JR, Gyacskov I et al. Deep learning segmentation of general interventional tools in two-dimensional ultrasound images. Med Phys 2020; 47: 4956-4970.

25 

Golshan M, Karimi D, Mahdavi S et al. Automatic detection of brachytherapy seeds in 3D ultrasound images using a convolutional neural network. Phys Med Biol 2020; 65: 035016.

26 

Zhang D, Yang Z, Jiang S et al. Automatic segmentation and applicator reconstruction for CT-based brachytherapy of cervical cancer using 3D convolutional neural networks. J Appl Clin Med Phys 2020; 21: 158-169.

27 

Weishaupt LL, Sayed HK, Mao X et al. Approaching automated applicator digitization from a new angle: Using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy. Brachytherapy 2022; 21: 520-531.

28 

Isensee F, Petersen J, Klein A et al. nnU-Net: self-adapting framework for U-Netbased medical image segmentation. arXiv:180910486, 2018.

29 

Yeghiazaryan V, Voiculescu I. Family of boundary overlap metrics for the evaluation of medical image segmentation. J Med Imaging (Bellingham) 2018; 5: 015006.

30 

Potter R, Haie-Meder C, Van Limbergen E et al. Recommendations from gynaecological (GYN) GEC ESTRO working group (II): concepts and terms in 3D image-based treatment planning in cervix cancer brachytherapy-3D dose volume parameters and aspects of 3D image-based anatomy, radiation physics, radiobiology. Radiother Oncol 2006; 78: 67-77.

31 

Deufel CL, Tian S, Yan BB et al. Automated applicator digitization for high-dose-rate cervix brachytherapy using image thresholding and density-based clustering. Brachytherapy 2020; 19: 111-118.

32 

Hu H, Yang Q, Li J et al. Deep learning applications in automatic segmentation and reconstruction in CT-based cervix brachytherapy. J Contemp Brachytherapy 2021; 13: 325-330.

33 

Shaaer A, Paudel M, Smith M et al. Deep-learning-assisted algorithm for catheter reconstruction during MR-only gynecological interstitial brachytherapy. J Appl Clin Med Phys 2022; 23: e13494.

34 

Moradi MM, Siavashpour Z, Takhtardeshir S et al. Fully automatic reconstruction of prostate high dose rate brachytherapy interstitial needles by using two phases deep learning based segmentation and object tracking algorithms, April 2022. 10.21203/rs.3.rs-1590410/v1.

35 

Peroni M, Spadea MF, Riboldi M et al. Validation of automatic contour propagation for 4D treatment planning using multiple metrics. Technol Cancer Res Treat 2013; 12: 501-510.

36 

Yoganathan SA, Paul SN, Paloor S et al. Automatic segmentation of magnetic resonance images for high-dose-rate cervical cancer brachytherapy using deep learning. Med Phys 2022; 49: 1571-1584.

37 

Schindel J, Zhang W, Bhatia SK et al. Dosimetric impacts of applicator displacements and applicator reconstruction-uncertainties on 3D image-guided brachytherapy for cervical cancer. J Contemp Brachytherapy 2013; 5: 250-257.

Copyright: © 2023 Termedia Sp. z o. o. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (http://creativecommons.org/licenses/by-nc-sa/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.
 
Quick links
© 2024 Termedia Sp. z o.o.
Developed by Bentus.