Automatic contouring system for cervical cancer using convolutional neural networks

Rhee, Dong Joo; Jhingran, Anuja; Rigaud, B.; Netherton, Tucker; Cárdenas, Carlos; Zhang, Lifei; Vedam, Sastry; Kry, Stephen F; Brock, Kristy K.; Shaw, William V.; O’Reilly, Frederika; Parkes, Jeannette; Burger, Hester; Fakie, Nazia; Trauernicht, Christoph; Simonds, Hannah; Court, Laurence Edward

doi:10.1002/mp.14467

Cited by 55 publications

(88 citation statements)

References 45 publications

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“…However, despite promising segmentation results their framework was only evaluated on patient scans from a single institution. In contrast, Rhee et al [94] used a VNet model to generate CT treatment plans and reported that their algorithm achieved on average 80%, 97% and 90% clinical acceptance rate for primary CTVs, OARs and bony structures respectively. Their framework was validated on 30 cervical cancer patients scanned across three hospitals.…”

Section: Cervical Cancermentioning

confidence: 99%

“…MRI is more accurate in the diagnosis, staging and treatment planning of rectal cancer compared with CT, and also provides quantitative tumor assessment, which can inform treatment response assessment and disease outcomes [170]. Although in recent years numerous studies were published for automatic contouring of pelvic tumors [94,[171][172][173][174], only a few were reported to address rectal cancer [27,146,175]. Based on our article search, 9 studies incorporated DL for rectal cancer segmentation applications (CT: 2, MRI: 6, MRI/CT: 1) (Table 1).…”

Section: Rectal Cancermentioning

confidence: 99%

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Automatic Segmentation of Pelvic Cancers using Deep Learning: State-of-the-Art Approaches and Challenges

Kalantar

Lin

Winfield

et al. 2021

Preprint

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The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides ground for technology development for computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive and clinically-oriented overview of DL-based segmentation studies for bladder, prostate, cervical and rectal cancers, highlighting the key findings, challenges and limitations.

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Section: Cervical Cancermentioning

confidence: 99%

Section: Rectal Cancermentioning

confidence: 99%

Automatic Segmentation of Pelvic Cancers using Deep Learning: State-of-the-Art Approaches and Challenges

Kalantar

Lin

Winfield

et al. 2021

Preprint

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“…In this study, we propose a fully automated coarse-tofine segmentation approach using convolutional neural networks (CNNs) to accurately and efficiently segment OARs in female pelvic MR images. While CT-based pelvic OAR segmentation has been actively studied, [4][5][6][7][8] there are few studies of automatic segmentation of pelvic MR images, with most existing studies focused on male pelvic OARs for prostate cancer diagnosis and treatment. 9 Segmenting OARs in MR images may be considered easier than CT given superior soft tissue image contrast.…”

Section: Introductionmentioning

confidence: 99%

Fully automated multiorgan segmentation of female pelvic magnetic resonance images with coarse‐to‐fine convolutional neural network

et al. 2021

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Purpose Brachytherapy combined with external beam radiotherapy (EBRT) is the standard treatment for cervical cancer and has been shown to improve overall survival rates compared to EBRT only. Magnetic resonance (MR) imaging is used for radiotherapy (RT) planning and image guidance due to its excellent soft tissue image contrast. Rapid and accurate segmentation of organs at risk (OAR) is a crucial step in MR image‐guided RT. In this paper, we propose a fully automated two‐step convolutional neural network (CNN) approach to delineate multiple OARs from T2‐weighted (T2W) MR images. Methods We employ a coarse‐to‐fine segmentation strategy. The coarse segmentation step first identifies the approximate boundary of each organ of interest and crops the MR volume around the centroid of organ‐specific region of interest (ROI). The cropped ROI volumes are then fed to organ‐specific fine segmentation networks to produce detailed segmentation of each organ. A three‐dimensional (3‐D) U‐Net is trained to perform the coarse segmentation. For the fine segmentation, a 3‐D Dense U‐Net is employed in which a modified 3‐D dense block is incorporated into the 3‐D U‐Net‐like network to acquire inter and intra‐slice features and improve information flow while reducing computational complexity. Two sets of T2W MR images (221 cases for MR1 and 62 for MR2) were taken with slightly different imaging parameters and used for our network training and test. The network was first trained on MR1 which was a larger sample set. The trained model was then transferred to the MR2 domain via a fine‐tuning approach. Active learning strategy was utilized for selecting the most valuable data from MR2 to be included in the adaptation via transfer learning. Results The proposed method was tested on 20 MR1 and 32 MR2 test sets. Mean ± SD dice similarity coefficients are 0.93 ± 0.04, 0.87 ± 0.03, and 0.80 ± 0.10 on MR1 and 0.94 ± 0.05, 0.88 ± 0.04, and 0.80 ± 0.05 on MR2 for bladder, rectum, and sigmoid, respectively. Hausdorff distances (95th percentile) are 4.18 ± 0.52, 2.54 ± 0.41, and 5.03 ± 1.31 mm on MR1 and 2.89 ± 0.33, 2.24 ± 0.40, and 3.28 ± 1.08 mm on MR2, respectively. The performance of our method is superior to other state‐of‐the‐art segmentation methods. Conclusions We proposed a two‐step CNN approach for fully automated segmentation of female pelvic MR bladder, rectum, and sigmoid from T2W MR volume. Our experimental results demonstrate that the developed method is accurate, fast, and reproducible, and outperforms alternative state‐of‐the‐art methods for OAR segmentation significantly (p < 0.05).

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“…Deep learning (DL) has been applied with success in proofs of concept across biomedical imaging modalities and medical specialties [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] . DL models can classify images by disease or structure and can segment, track, and measure structures within images.…”

Section: Introductionmentioning

confidence: 99%

ENRICH: Exploiting Image Similarity to Maximize Efficient Machine Learning in Medical Imaging

Chinn

Arora

Arnaout

et al. 2021

Preprint

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Deep learning (DL) requires labeled data. Labeling medical images requires medical expertise, which is often a bottleneck. It is therefore useful to prioritize labeling those images that are most likely to improve a model's performance, a practice known as instance selection. Here we introduce ENRICH, a method that selects images for labeling based on how much novelty each image adds to the growing training set. In our implementation, we use cosine similarity between autoencoder embeddings to measure that novelty. We show that ENRICH achieves nearly maximal performance on classification and segmentation tasks using only a fraction of available images, and outperforms the default practice of selecting images at random. We also present evidence that instance selection may perform categorically better on medical vs. non-medical imaging tasks. In conclusion, ENRICH is a simple, computationally efficient method for prioritizing images for expert labeling for DL.

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Automatic contouring system for cervical cancer using convolutional neural networks

Cited by 55 publications

References 45 publications

Automatic Segmentation of Pelvic Cancers using Deep Learning: State-of-the-Art Approaches and Challenges

Automatic Segmentation of Pelvic Cancers using Deep Learning: State-of-the-Art Approaches and Challenges

Fully automated multiorgan segmentation of female pelvic magnetic resonance images with coarse‐to‐fine convolutional neural network

ENRICH: Exploiting Image Similarity to Maximize Efficient Machine Learning in Medical Imaging

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