Evaluation of positional plagiocephaly: Conventional anthropometric measurement versus laser scanning method

Nahles, Susanne; Klein, Martin; Yacoub, A.; Neyer, Julia

doi:10.1016/j.jcms.2017.10.010

Cited by 25 publications

(21 citation statements)

References 55 publications

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“…In the clinic, the standard diagnostic procedure uses a sliding caliper or a craniometer as a tool for the manual measurement of the cranial deformity; this method is inconvenient and possibly inaccurate [ 10 , 11 ]. Although three-dimensional (3D) cranial scans are considered the gold standard in diagnosis and can provide more accurate and reliable information, they require an expensive laser scanner with a head cap and a skilled operator [ 12 , 13 , 14 ]. Other 3D imaging techniques, such as stereophotogrammetry and procedures involving structured light patterns, are becoming increasingly important for the diagnosis of skull abnormalities [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach

et al. 2020

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Positional cranial deformities are relatively common conditions, characterized by asymmetry and changes in skull shape. Although three-dimensional (3D) scanning is the gold standard for diagnosing such deformities, it requires expensive laser scanners and skilled maneuvering. We therefore developed an inexpensive, fast, and convenient screening method to classify cranial deformities in infants, based on single two-dimensional vertex cranial images. In total, 174 measurements from 80 subjects were recorded. Our screening software performs image processing and machine learning-based estimation related to the deformity indices of the cranial ratio (CR) and cranial vault asymmetry index (CVAI) to determine the severity levels of brachycephaly and plagiocephaly. For performance evaluations, the estimated CR and CVAI values were compared to the reference data obtained using a 3D cranial scanner. The CR and CVAI correlation coefficients obtained via support vector regression were 0.85 and 0.89, respectively. When the trained model was evaluated using the unseen test data for the three CR and three CVAI classes, an 86.7% classification accuracy of the proposed method was obtained for both brachycephaly and plagiocephaly. The results showed that our method for screening cranial deformities in infants could aid clinical evaluations and parental monitoring of the progression of deformities at home.

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Section: Introductionmentioning

confidence: 99%

Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach

et al. 2020

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“…Despite its importance, commonly used methodology include visual assessment and manual measurements using measuring tape and callipers [4][5][6]. These methodologies have low accuracy and provide very limited information [7]. Radiological images (Computed Tomography -CTand Magnetic Resonance Imaging -MRI-) are vital for the diagnosis of craniosynostosis and they are considered the gold standard for yielding 3D models [6].…”

Section: Introductionmentioning

confidence: 99%

Automatic Low-Cost Tool for Head 3D Modelling and Cranial Deformation Analysis in Infants

Barbero-García¹,

Lerma²,

Miranda³

2019

Proceedings of 3DBODY.TECH 2019 - 10th International Conference and Exhibition on 3D Body Scanning and Processing Technologies,

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Cranial deformation is a problem that affects an important number of infants. It requires different treatments, varying from repositioning techniques to surgery depending on the leading cause. The evaluation of the deformation is usually carried out using simple but low-accuracy methodologies such as visual assessment and clinical measurements. Accurate methodologies such as radiological tests and combinations of cameras and scanner are available but they are invasive and/or costly. A patent-pending smartphone-based photogrammetric tool for the analysis of cranial deformation is presented. The methodology allows the generation of head three-dimensional (3D) models of infants automatically. It is non-invasive, low-cost and can be operated by users without any photogrammetric knowledge. The presented tool consists of a smartphone app, a coded cap that is fitted to the head of the infant and processing software that yields the 3D models. The data acquisition can be easily carried out during the routine clinical consultation. The patient is held by an adult while the application is used to obtain the data around the infant's head. The process is similar to a video recording, and the app guides the user during the process. The tool can tolerate the patient's movement so no immobilization or sedation is required. The whole data acquisition process can be completed in a couple of minutes. After the acquisition stage, the data is processed to obtain a 3D model of the patient's head. The accuracy and repeatability of the process have been tested under ideal conditions. The obtained accuracy was found to be better than 1 mm. The methodology has also been tested in real conditions obtaining models useful for the evaluation of different types of cranial deformation. The tool can be easily implemented in clinical routine. It requires minimum resources and provides a significant improvement of the techniques currently used for cranial deformation assessment.

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“…The diagnosis and evaluation are performed based on cranial asymmetry indexes estimated from anthropometric skull measurements. These measurements are usually extracted manually using calipers [8]. However, a high intra-and inter-observer variability of that method can be expected since accuracy of measurement will vary between investigators.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based detection of anthropometric landmarks in 3D infants head models

Torres

Oliveira

Veloso

et al. 2019

Medical Imaging 2019: Computer-Aided Diagnosis

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Deformational plagiocephaly (DP) is a cranial deformity characterized by an asymmetrical distortion of an infant's skull. The diagnosis and evaluation of DP are performed using cranial asymmetry indexes obtained from cranial measurements, which can be estimated using anthropometric landmarks of the infant's head. However, manual labeling of these landmarks is a time-consuming and tedious task, being also prone to observer variability. In this paper, a novel framework to automatically detect anthropometric landmarks of 3D infant's head models is described. The proposed method is divided into two stages: (i) unfolding of the 3D head model surface; and (ii) landmarks' detection through a deep learning strategy. In the first stage, an unfolding strategy is used to transform the 3D mesh of the head model to a flattened 2D version of it. From the flattened mesh, three 2D informational maps are generated using specific head characteristics. In the second stage, a deep learning strategy is used to detect the anthropometric landmarks in a 3-channel image constructed using the combination of informational maps. The proposed framework was validated in fifteen 3D synthetic models of infant's head, being achieved, in average for all landmarks, a mean distance error of 3.5 mm between the automatic detection and a manually constructed ground-truth. Moreover, the estimated cranial measurements were comparable to the ones obtained manually, without statistically significant differences between them for most of the indexes. The obtained results demonstrated the good performance of the proposed method, showing the potential of this framework in clinical practice.

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Evaluation of positional plagiocephaly: Conventional anthropometric measurement versus laser scanning method

Cited by 25 publications

References 55 publications

Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach

Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach

Automatic Low-Cost Tool for Head 3D Modelling and Cranial Deformation Analysis in Infants

Deep learning-based detection of anthropometric landmarks in 3D infants head models

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