Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy

Outeiral, Roque Rodríguez; González, P.; Schaake, Eva E.; Heide, Uulke A. van der; Simões, Rita

doi:10.1186/s13014-023-02283-8

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…Zabihollahy et al ( 64 , 65 ) evaluated 2D Attention UNet, 3D UNet and 3D Dense UNet in the segamentaiton of CTVs and ORAs by using MRI images. Rodríguez Outeiral et al ( 29 ) used nnUNet to segment cervical cancer and evaluated model performance based on FIGO stage and GTV volume. Lin et al ( 66 ) used DeepLab V3+ as the pre-trained model and then adjusted the training data size and fine-tuning layers through transfer learning.…”

Section: Current State Of Cervical Cancer Segmentation Methods For Di...mentioning

confidence: 99%

Cervical cancer segmentation based on medical images: a literature review

Wang,

Feng,

Huang

et al. 2024

Quant Imaging Med Surg

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Background and Objective Cervical cancer clinical target volume (CTV) outlining and organs at risk segmentation are crucial steps in the diagnosis and treatment of cervical cancer. Manual segmentation is inefficient and subjective, leading to the development of automated or semi-automated methods. However, limitation of image quality, organ motion, and individual differences still pose significant challenges. Apart from numbers of studies on the medical images’ segmentation, a comprehensive review within the field is lacking. The purpose of this paper is to comprehensively review the literatures on different types of medical image segmentation regarding cervical cancer and discuss the current level and challenges in segmentation process. Methods As of May 31, 2023, we conducted a comprehensive literature search on Google Scholar, PubMed, and Web of Science using the following term combinations: “cervical cancer images”, “segmentation”, and “outline”. The included studies focused on the segmentation of cervical cancer utilizing computed tomography (CT), magnetic resonance (MR), and positron emission tomography (PET) images, with screening for eligibility by two independent investigators. Key Content and Findings This paper reviews representative papers on CTV and organs at risk segmentation in cervical cancer and classifies the methods into three categories based on image modalities. The traditional or deep learning methods are comprehensively described. The similarities and differences of related methods are analyzed, and their advantages and limitations are discussed in-depth. We have also included experimental results by using our private datasets to verify the performance of selected methods. The results indicate that the residual module and squeeze-and-excitation blocks module can significantly improve the performance of the model. Additionally, the segmentation method based on improved level set demonstrates better segmentation accuracy than other methods. Conclusions The paper provides valuable insights into the current state-of-the-art in cervical cancer CTV outlining and organs at risk segmentation, highlighting areas for future research.

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Section: Current State Of Cervical Cancer Segmentation Methods For Di...mentioning

confidence: 99%

Cervical cancer segmentation based on medical images: a literature review

Wang,

Feng,

Huang

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

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“…There are plenty of studies on automated segmentation of primary tumors using DL algorithms [7,8,9,10,11]. Several approaches have also been introduced for BM segmentation on MRI using DL [12].…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post Stereotactic Radiosurgery

Kanakarajan,

De Baene,

Hanssens

et al. 2023

Preprint

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Background: Brain metastases (BM) represent the most common intracranial tumor in adults. An estimated 20% of all patients with cancer will develop BM. Stereotactic Radiosurgery (SRS) is a major treatment option for BM. For SRS treatment planning and outcome evaluation, magnetic resonance imaging (MRI) are acquired before and at multiple stages during the follow-up. Accurate segmentation of brain tumors on MRI is crucial for treatment planning and response evaluation. Detection and segmentation of BM is a tedious and time-consuming task for many radiologists that could be optimized with machine learning methods. Previous studies evaluated the segmentation performance of several deep learning algorithms, but focused mainly on training and testing the models on the planning MR images only. The purpose of this study was to investigate a well-known deep learning approach (nnU-Net) for BM segmentation and to evaluate its performance on both planning MR images and follow-up MR images based on training on planning MR images only and testing with both planning MR and follow-up MR images. Method: Pre-treatment contrast-enhanced T1-weighted brain MRIs(i.e. the planning MRI) were collected retrospectively for 263 patients with BM. Scans were made as part of clinical care at the Gamma Knife Center of the Elisabeth-TweeSteden Hospital (Tilburg, the Netherlands). This total of 263 patients were split into 203 patients for model training/validation and 60 patients for testing. For these 60 patients used for testing, the post treatment contrast-enhanced follow-up T1-weighted brain MRI scans(i.e. follow-up MRI) were also retrospectively collected. These 60 patients who were part of the testing set are from the set of patients included in the Cognition And Radiation Study A(CAR-Study A) at ETZ. The follow-up (FU) scans were made at 3, 6, 9, 12, 15, and 21 months after treatment. The nnU-Net model was trained with the planning MR images, and then tested separately against the planning and follow-up MR images. Results: When tested with planning MR images, the model obtained a dice similarity coefficient (DSC) of 0.940, a False Negative Rate (FNR) of 0.065 and a sensitivity of 0.934. When tested with the follow-up MR images 3, 6, 9, 12, 15 and 21 months after treatment , the model obtained, respectively, a DSC of 0.759, 0.667, 0.604, 0.589, 0.666 and 0.574, an FNR of 0.288, 0.379, 0.445, 0.470, 0.409, and 0.487 and a sensitivity of 0.711, 0.620, 0.554, 0.529, 0.590, and 0.512. Conclusion: The model achieved a good performance score for planning MR images. The nnU-Net model can automatically detect and segment brain metastases with high sensitivity, and low FNR. Though there is a decline in the DSC and an increase in the FNR of the model for the follow-up MR images, the algorithm could be a beneficial tool for clinicians and assist them for diagnosis, treatment planning and treatment response evaluations during follow-ups of BM patients.

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A deep learning‐based 3D Prompt‐nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma

Xue,

Liang,

Wang

et al. 2024

J Applied Clin Med Phys

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PurposeTo create and evaluate a three‐dimensional (3D) Prompt‐nnUnet module that utilizes the prompts‐based model combined with 3D nnUnet for producing the rapid and consistent autosegmentation of high‐risk clinical target volume (HR CTV) and organ at risk (OAR) in high‐dose‐rate brachytherapy (HDR BT) for patients with postoperative endometrial carcinoma (EC).Methods and materialsOn two experimental batches, a total of 321 computed tomography (CT) scans were obtained for HR CTV segmentation from 321 patients with EC, and 125 CT scans for OARs segmentation from 125 patients. The numbers of training/validation/test were 257/32/32 and 87/13/25 for HR CTV and OARs respectively. A novel comparison of the deep learning neural network 3D Prompt‐nnUnet and 3D nnUnet was applied for HR CTV and OARs segmentation. Three‐fold cross validation and several quantitative metrics were employed, including Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of Hausdorff distance (HD95%), and intersection over union (IoU).ResultsThe Prompt‐nnUnet included two forms of parameters Predict‐Prompt (PP) and Label‐Prompt (LP), with the LP performing most similarly to the experienced radiation oncologist and outperforming the less experienced ones. During the testing phase, the mean DSC values for the LP were 0.96 ± 0.02, 0.91 ± 0.02, and 0.83 ± 0.07 for HR CTV, rectum and urethra, respectively. The mean HD values (mm) were 2.73 ± 0.95, 8.18 ± 4.84, and 2.11 ± 0.50, respectively. The mean HD95% values (mm) were 1.66 ± 1.11, 3.07 ± 0.94, and 1.35 ± 0.55, respectively. The mean IoUs were 0.92 ± 0.04, 0.84 ± 0.03, and 0.71 ± 0.09, respectively. A delineation time < 2.35 s per structure in the new model was observed, which was available to save clinician time.ConclusionThe Prompt‐nnUnet architecture, particularly the LP, was highly consistent with ground truth (GT) in HR CTV or OAR autosegmentation, reducing interobserver variability and shortening treatment time.

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Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy

Cited by 8 publications

References 16 publications

Cervical cancer segmentation based on medical images: a literature review

Cervical cancer segmentation based on medical images: a literature review

Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post Stereotactic Radiosurgery

A deep learning‐based 3D Prompt‐nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma

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