Malocclusion Treatment Planning via PointNet Based Spatial Transformation Network

Li, Xiaoshuang; Bi, Lei; Kim, Jinman; Li, Tingyao; Tian, Ye; Sheng, Bin; Feng, Dagan

doi:10.1007/978-3-030-59716-0_11

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…We implemented three methods to predict postoperative facial appearance based on the bony movement. They were finite element model with realistic lip sliding effect (FEM-RLSE) [4], FC-Net [6], and our ACMT-Net. While the implementation details of FEM-RLSE and FC-Net can be found in [4,6], the implementation detail of ACMT-Net is described below.…”

Section: Implementation and Evaluation Methodsmentioning

confidence: 99%

“…Deep learning-based approaches have been recently proposed to automate and accelerate the surgical simulation. Li et al [6] proposed a spatial transformer network based on the PointNet [11] to predict tooth displacement for malocclusion treatment planning. Xiao et al [18] developed a self-supervised deep learning framework to estimate normalized facial bony models to guide orthognathic surgical planning.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement

Fang

Kim

et al. 2022

Lecture Notes in Computer Science

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Simulating facial appearance change following bony movement is a critical step in orthognathic surgical planning for patients with jaw deformities. Conventional biomechanics-based methods such as the finite-element method (FEM) are labor intensive and computationally inefficient. Deep learning-based approaches can be promising alternatives due to their high computational efficiency and strong modeling capability. However, the existing deep learning-based method ignores the physical correspondence between facial soft tissue and bony segments and thus is significantly less accurate compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to estimate the facial appearance by transforming the bony movement to facial soft tissue through a point-to-point attentive correspondence matrix. Experimental results on patients with jaw deformity show that our proposed method can achieve comparable facial change prediction accuracy compared with the state-of-the-art FEMbased approach with significantly improved computational efficiency.

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Section: Implementation and Evaluation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement

Fang

Kim

et al. 2022

Lecture Notes in Computer Science

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“…processing task (Cui et al 2021;Qiu et al 2022;Ma et al 2020;Cui et al 2022) and the tooth alignment target prediction (Li et al 2020a;Wei et al 2020;Wang et al 2022). Due to its complexity and challenge, the data-driven tooth motion generation method remains a blank area.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Tooth Motion Diffusion Model in Digital Orthodontics

Fan,

Wei,

Wang

et al. 2024

AAAI

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Tooth motion generation is an essential task in digital orthodontic treatment for precise and quick dental healthcare, which aims to generate the whole intermediate tooth motion process given the initial pathological and target ideal tooth alignments. Most prior works for multi-agent motion planning problems usually result in complex solutions. Moreover, the occlusal relationship between upper and lower teeth is often overlooked. In this paper, we propose a collaborative tooth motion diffusion model. The critical insight is to remodel the problem as a diffusion process. In this sense, we model the whole tooth motion distribution with a diffusion model and transform the planning problem into a sampling process from this distribution. We design a tooth latent representation to provide accurate conditional guides consisting of two key components: the tooth frame represents the position and posture, and the tooth latent shape code represents the geometric morphology. Subsequently, we present a collaborative diffusion model to learn the multi-tooth motion distribution based on inter-tooth and occlusal constraints, which are implemented by graph structure and new loss functions, respectively. Extensive qualitative and quantitative experiments demonstrate the superiority of our framework in the application of orthodontics compared with state-of-the-art methods.

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“…Most of the existing meth-ods for automatic tooth arrangement are found in proprietary software, and their details are not easily available. Recently, Wei et al and Li et al proposed two deep-learning based methods for such a task [9,7]. While offering good-quality tooth alignments on most studied common cases, these methods require large datasets of pre and post treatment tooth arrangements for their training.…”

Section: Introductionmentioning

confidence: 99%

Pose estimation of teeth in pathological dental models

Maxime¹,

Lafourcade²,

Puech³

et al. 2022

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In this paper, we present a method to estimate the pose of teeth in pathological dental models. Given a 3D pathological model, we aim at computing the orientation and position of each tooth with respect to a healthy dentition. This deviation from a healthy dentition is informative of the orthodontic disorders of the patient. The proposed method is based on the registration of a reference dental model on a patient-specific 3D segmented mesh. Then, dental features, such as the patient's arch forms, are derived. These features, combined with registration information, allow our system to propose a plausible target dental arrangement from which the pose of each tooth is derived.The key contributions of this work are (a) the use of a registered reference model to automatically estimate a plausible target dental arrangement for a given patient, and (b) the derivation of teeth poses from registration information and the target arrangement.

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Malocclusion Treatment Planning via PointNet Based Spatial Transformation Network

Cited by 7 publications

References 15 publications

Deep Learning-Based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement

Deep Learning-Based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement

Collaborative Tooth Motion Diffusion Model in Digital Orthodontics

Pose estimation of teeth in pathological dental models

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