Reconstructing and resizing 3D images from DICOM files

Fajar, Aziz; Sarno, Riyanarto; Fatichah, Chastine; Fahmi, Achmad

doi:10.1016/j.jksuci.2020.12.004

Cited by 14 publications

(11 citation statements)

References 27 publications

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“…There already exist many CNN processing DICOM data and are available for use [12,13,15]. Researchers this year have already achieved significant milestones in multiclass CBCT image segmentation for orthodontics with Deep Learning.…”

Section: Discussionmentioning

confidence: 99%

“…They omitted subjects with fractures. Each CT output was in digital imaging and communication in medicine (DICOM) format [12,13]. The extracted femur and mandible data were preprocessed to create a 3D voxel inserted into a neural network model.…”

Section: Overview Of Used Artificial Intelligence For Forensic Age and Sex Determinationmentioning

confidence: 99%

“…The methodology chapter presents the novel clinical workflow based on the implementation of three-dimensional convolutional neural network (3D CNN) algorithms [8][9][10]. The input is full head cone-beam computer tomography scans (CBCT) in the Digital Imaging and Communications in Medicine format (DICOM) [10][11][12][13][14][15]. The methodology chapter describes technical data preparation for 3D CNN utilization in the following practical aspects from forensic medicine:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of Advanced Artificial Intelligence in Forensic Medicine

Thurzo¹,

Kosnáčová²,

Kurilová³

et al. 2021

Preprint

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Three-dimensional convolutional neural networks (3D CNN) as a type of artificial intelligence (AI) are powerful in image processing and recognition using deep learning to perform generative and descriptive tasks. The advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. 3D CNN are used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim of this article was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers. With emphasis activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance the forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore and present 3D CNN method for forensic research design concept in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

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Section: Discussionmentioning

confidence: 99%

Section: Overview Of Used Artificial Intelligence For Forensic Age and Sex Determinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Use of Advanced Artificial Intelligence in Forensic Medicine

Thurzo¹,

Kosnáčová²,

Kurilová³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The methodology chapter presents the novel clinical workflow based on implementing three-dimensional convolutional neural network (3D CNN) algorithms [7][8][9]. The input is full head cone-beam computer tomography scans (CBCT) in the Digital Imaging and Communications in Medicine format (DICOM) [9][10][11][12][13][14]. The methodology chapter describes technical data preparation for 3D CNN utilization in the following practical aspects from forensic medicine:…”

Section: Introductionmentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

et al. 2021

View full text Add to dashboard Cite

Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

show abstract

“…The DICOM files generated by computed tomography can be converted to 3D reconstruction images in STL files using various software such as ITKsnap, 3D slicer, ImageJ, TurtleSeg, InVesalius 3 etc. The current reconstruction of 3D images from DICOM (Digital Imaging and Communications in Medicine) files requires strict supervision for the reconstructed images to have the same metadata, including slice thickness, spacing between slices, and image resolution [ 3 ].…”

mentioning

confidence: 99%

Three-dimensional computed tomography reconstructions: A tool for veterinary anatomy education

Choudhary

2021

Annals of Medicine &Amp; Surgery

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Reconstructing and resizing 3D images from DICOM files

Cited by 14 publications

References 27 publications

Use of Advanced Artificial Intelligence in Forensic Medicine

Use of Advanced Artificial Intelligence in Forensic Medicine

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Three-dimensional computed tomography reconstructions: A tool for veterinary anatomy education

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