Anthropometric Landmark Detection in 3D Head Surfaces Using a Deep Learning Approach

Torres, Helena R.; Morais, Pedro; Fritze, Anne; Oliveira, Bruno; Veloso, Fernando; Rüdiger, Mario; Fonseca, Jaime C.; Vilaça, João L.

doi:10.1109/jbhi.2020.3035888

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…There has also been an extensive use of anatomical head structure estimation using available measurements. Torres et al [ 41 ] proposed a deep-learning landmark detection method focused on 3D infant head surface shapes. Xiao et al [ 42 ] introduced a method that relies on head-surface sampling to estimate the location of certain neurocranial landmarks.…”

Section: Related Workmentioning

confidence: 99%

Scalp surface estimation and head registration using sparse sampling and 3D statistical models

Schlesinger,

Kundu,

Isaev

et al. 2024

Computers in Biology and Medicine

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Section: Related Workmentioning

confidence: 99%

Scalp surface estimation and head registration using sparse sampling and 3D statistical models

Schlesinger,

Kundu,

Isaev

et al. 2024

Computers in Biology and Medicine

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“…After creating the 2D representative maps, a regression CNN was applied to estimate probability maps for the landmarks' localization. Similar to [10], a multi-branch approach was implemented where a CNN was applied to process each 2D map individually. This generates a set of feature maps 𝑉 𝑟 , with 𝑟 ∈ {1 … 4}, that are afterward concatenated into a global one.…”

Section: Regression Cnn For Probability Maps Estimationmentioning

confidence: 99%

“…In fact, deep learning (DL) showed superior performance over the conventional machine learning strategies or registration-based approaches [9]. Our team have already demonstrated the added-value of the DL techniques to detect some landmarks in 3D infant's head surfaces [10]. Overall, the proposed strategy is a two-stage method that includes creation of 2D maps representative of the 3D head model and detection of anthropometric landmarks in the 2D maps using a DL strategy.…”

mentioning

confidence: 98%

3D Facial Landmark Localization for cephalometric analysis

Torres

Morais

Fritze

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Cephalometric analysis is an important and routine task in the medical field to assess craniofacial development and to diagnose cranial deformities and midline facial abnormalities. The advance of 3D digital techniques potentiated the development of 3D cephalometry, which includes the localization of cephalometric landmarks in the 3D models. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra/inter-observer variability. In this paper, a framework to automatically locate cephalometric landmarks in 3D facial models is presented. The landmark detector is divided into two stages: (i) creation of 2D maps representative of the 3D model; and (ii) landmarks' detection through a regression convolutional neural network (CNN). In the first step, the 3D facial model is transformed to 2D maps retrieved from 3D shape descriptors. In the second stage, a CNN is used to estimate a probability map for each landmark using the 2D representations as input. The detection method was evaluated in three different datasets of 3D facial models, namely the Texas 3DFR, the BU3DFE, and the Bosphorus databases. An average distance error of 2.3, 3.0, and 3.2 mm were obtained for the landmarks evaluated on each dataset. The obtained results demonstrated the accuracy of the method in different 3D facial datasets with a performance competitive to the state-of-the-art methods, allowing to prove its versability to different 3D models.Clinical Relevance-Overall, the performance of the landmark detector demonstrated its potential to be used for 3D cephalometric analysis. I. INTRODUCTIONCephalometric analysis refers to the assessment of the craniofacial structure to evaluate its growth and development and to diagnose cranial deformities, midline facial abnormalities, and orthodontic problems [1]. Traditionally, 2D radiographs are used for cephalometric analysis, but Computed Tomography (CT) and Magnetic Resonance (MR) imaging systems have been enabling 3D cephalometry [2]. More recently, some works proposed to use 3D digital models (e.g. laser scans) to perform the cephalometric analysis [3]. To

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Graph Convolutional Network with Probabilistic Spatial Regression: Application to Craniofacial Landmark Detection from 3D Photogrammetry

Elkhill

LeBeau

French

et al. 2022

Lecture Notes in Computer Science

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Anthropometric Landmark Detection in 3D Head Surfaces Using a Deep Learning Approach

Cited by 12 publications

References 32 publications

Scalp surface estimation and head registration using sparse sampling and 3D statistical models

Scalp surface estimation and head registration using sparse sampling and 3D statistical models

3D Facial Landmark Localization for cephalometric analysis

Graph Convolutional Network with Probabilistic Spatial Regression: Application to Craniofacial Landmark Detection from 3D Photogrammetry

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