Tooth instance segmentation based on capturing dependencies and receptive field adjustment in cone beam computed tomography

Dou, Wenhan; Gao, Shanshan; Mao, Deqian; Dai, Honghao; Zhang, Chenhao; Zhou, Yuanfeng

doi:10.1002/cav.2100

Cited by 9 publications

(3 citation statements)

References 25 publications

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“… Chen et al (2020) applied a modified V-Net architecture to handle tooth regions and tooth surface prediction simultaneously. Dou et al (2022) integrated an attention mechanism and a self-regulatory mechanism into the V-Net network structure, achieving higher Dice (0.952) and IoU (0.902) scores and lower ASSD (0.15 mm) values than did the methods proposed by Chen et al (2020) .…”

Section: Resultsmentioning

confidence: 99%

Semi or fully automatic tooth segmentation in CBCT images: a review

Zheng,

Gao,

Zhou

et al. 2024

PeerJ Computer Science

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Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions.

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Section: Resultsmentioning

confidence: 99%

Semi or fully automatic tooth segmentation in CBCT images: a review

Zheng,

Gao,

Zhou

et al. 2024

PeerJ Computer Science

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“…Furthermore, we present the Strip Mixed Aggregation Pyramid Pooling Module (SMAPPM) to extend the contextual representations and receptive fields. Compared with existing methods that alter receptive fields [16][17] [18][19] [20], our method is more straightforward to implement, with lower computational overhead and latency. Using these components, we introduce a real-time semantic segmentation model named BENet, successfully striking a balance between inference speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

BENet: boundary-enhanced network for real-time semantic segmentation

Lei,

Chen,

et al. 2024

Vis Comput

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In the realm of real-time semantic segmentation, deep neural networks have demonstrated promising potential. However, current methods face challenges when it comes to accurately segmenting object boundaries and small objects. This limitation is partly attributed to the prevalence of convolutional neural networks, which often involve multiple sequential down-sampling operations, resulting in the loss of fine-grained details. To overcome this drawback, we introduce BENet, a real-time semantic segmentation network with a focus on enhancing object boundaries. The proposed BENet integrates two key components: the Boundary Extraction Module (BEM) and the Boundary Adaption Layer (BAL). The proposed BEM efficiently extracts boundary information, while the BAL guides the network using this information to preserve intricate details during the feature extraction process. Furthermore, to address the challenges associated with poor segmentation of elongated objects, we introduce the Strip Mixed Aggregation Pyramid Pooling Module (SMAPPM). This module employs strip pooling kernels to effectively expand the contextual representation and receptive field of the network, thereby enhancing overall segmentation performance. Our experiments conducted on a single RTX 3090 GPU show that our method achieves an mIoU of 79.4% at a speed of 45.5 FPS on the Cityscapes test set without ImageNet pre-training.

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“…Instance segmentation finds broad application across various domains, including but not limited to autonomous driving, intelligent monitoring, and medical image analysis [1]. TSDNet [79] proposes a new deep learning network for tooth segmentation that relies on cone beam computed tomography (CBCT) data acquisition and receptive field adjustment. The primary goal is to achieve automatic and precise instance segmentation of dental CBCT data.…”

Section: Introductionmentioning

confidence: 99%

SimpleMask: Parameter-free and Efficient Instance Segmentation

Liu,

Lu,

Gao

et al. 2023

Preprint

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To resolve the problem that the segmentation result of the full convolutional neural network in the Mask R-CNN model is not fine enough, and that the number of loss function hyperparameters is too large, leadings to the time and resource consumption of parameter adjustment, we propose a parameter-free and efficient instance segmentation model in this paper. Aiming at the problem that the Mask R-CNN model does not pay attention to sample features, the method of fusing the visual attention network in the ResNet50 backbone network is adopted to achieve self-adaptation and long-range correlation in self-attention, so that the model can precisely recognize the target location and effectively detect and segment the target. The U-Net network is introduced into the segmentation, and the image is processed by stepwise upsampling and downsampling, so that the network segmentation accuracy for the pixel mask is more accurate. Considering the parameter tuning problem of the instance segmentation task, a parameter-connected loss is recommended to simplify the complexity of model training parameter tuning and further enhance the detection and segmentation performance of the model. We conduct extensive experiments on three extensive baselines, i.e., MiniCOCO, Cityscapes and PASCAL VOC2012, to assess the validity of our model. The experimental findings demonstrate that (1) in the MiniCOCO dataset, a box AP of 35.1 and a mask AP of 32.0 are obtained. Compared with the most advanced mask2former algorithm, the box AP and mask AP are 1.7 and 2.2 higher, respectively. (2) The AP value on Cityscapes is 38.1. In comparison to alternative instance segmentation models, the mAP of each category has been greatly improved. (3) The generalization experiment of our model on the PASCAL VOC2012 dataset shows that the box mAP and mask mAP are 75.5 and 63.6, respectively, which are improved by 3.9 and 1.9, respectively, when contrasting with the Mask R-CNN model. Our model has significant advantages in both detection and segmentation. The code will be available at https://gitee.com/zhiweilu111/simple-mask/tree/master.

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Tooth instance segmentation based on capturing dependencies and receptive field adjustment in cone beam computed tomography

Cited by 9 publications

References 25 publications

Semi or fully automatic tooth segmentation in CBCT images: a review

Semi or fully automatic tooth segmentation in CBCT images: a review

BENet: boundary-enhanced network for real-time semantic segmentation

SimpleMask: Parameter-free and Efficient Instance Segmentation

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