Recurrent convolutional neural networks for mandible segmentation from computed tomography

Qiu, Bingjiang; Guo, Jiapan; Kraeima, Joep; Glas, H.H.; Borra, Ronald; Witjes, Max J. H.; Ooijen, Peter M. A. van

doi:10.48550/arxiv.2003.06486

Cited by 2 publications

(3 citation statements)

References 37 publications

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“…Moreover, accurate segmentation of head CT is challenging owing to the complexity of the anatomical structures, the low contrast of soft tissue, artifacts caused by mental implants, and variations between individual patients [6]. In specific, weak and false edges of condyles appearing in CT images adversely affect the accurate segmentation of the mandible [7]. Figure 1 shows examples of the difficulties in segmenting the mandible and maxilla.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1 shows examples of the difficulties in segmenting the mandible and maxilla. Automatic segmentation can improve efficiency and reliability, reducing segmentation time and clinician workload [7]. Numerous studies exist on automatic or semi-automatic segmentation of the mandible from CT scans, including OARs.…”

Section: Introductionmentioning

confidence: 99%

“…Both a faster regional CNN and attention U-Net for localization and segmentation have been introduced by Lei et al [24]. A recurrent segmentation CNN was proposed that embeds the CNN into a recurrent neural network for segmentation of the mandible from CT [7]. An attention mechanism, which has been advanced with deep learning models in computer vision, has been incorporated to U-Net for segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images

Park

Kim²,

Shim³

et al. 2022

Applied Sciences

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Sophisticated segmentation of the craniomaxillofacial bones (the mandible and maxilla) in computed tomography (CT) is essential for diagnosis and treatment planning for craniomaxillofacial surgeries. Conventional manual segmentation is time-consuming and challenging due to intrinsic properties of craniomaxillofacial bones and head CT such as the variance in the anatomical structures, low contrast of soft tissue, and artifacts caused by metal implants. However, data-driven segmentation methods, including deep learning, require a large consistent dataset, which creates a bottleneck in their clinical applications due to limited datasets. In this study, we propose a deep learning approach for the automatic segmentation of the mandible and maxilla in CT images and enhanced the compatibility for multi-center datasets. Four multi-center datasets acquired by various conditions were applied to create a scenario where the model was trained with one dataset and evaluated with the other datasets. For the neural network, we designed a hierarchical, parallel and multi-scale residual block to the U-Net (HPMR-U-Net). To evaluate the performance, segmentation with in-house dataset and with external datasets from multi-center were conducted in comparison to three other neural networks: U-Net, Res-U-Net and mU-Net. The results suggest that the segmentation performance of HPMR-U-Net is comparable to that of other models, with superior data compatibility.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images

Park

Kim²,

Shim³

et al. 2022

Applied Sciences

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Robust and Accurate Mandible Segmentation on Dental CBCT Scans Affected by Metal Artifacts Using a Prior Shape Model

Qiu

Wel

Kraeima

et al. 2021

JPM

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Accurate mandible segmentation is significant in the field of maxillofacial surgery to guide clinical diagnosis and treatment and develop appropriate surgical plans. In particular, cone-beam computed tomography (CBCT) images with metal parts, such as those used in oral and maxillofacial surgery (OMFS), often have susceptibilities when metal artifacts are present such as weak and blurred boundaries caused by a high-attenuation material and a low radiation dose in image acquisition. To overcome this problem, this paper proposes a novel deep learning-based approach (SASeg) for automated mandible segmentation that perceives overall mandible anatomical knowledge. SASeg utilizes a prior shape feature extractor (PSFE) module based on a mean mandible shape, and recurrent connections maintain the continuity structure of the mandible. The effectiveness of the proposed network is substantiated on a dental CBCT dataset from orthodontic treatment containing 59 patients. The experiments show that the proposed SASeg can be easily used to improve the prediction accuracy in a dental CBCT dataset corrupted by metal artifacts. In addition, the experimental results on the PDDCA dataset demonstrate that, compared with the state-of-the-art mandible segmentation models, our proposed SASeg can achieve better segmentation performance.

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Recurrent convolutional neural networks for mandible segmentation from computed tomography

Cited by 2 publications

References 37 publications

Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images

Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images

Robust and Accurate Mandible Segmentation on Dental CBCT Scans Affected by Metal Artifacts Using a Prior Shape Model

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