Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization

Souadih, Kamal; Belaid, Ahror; Salem, Douraı̈ed Ben; Conze, Pierre-Henri

doi:10.1007/s11517-019-02050-6

Cited by 17 publications

(4 citation statements)

References 29 publications

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“…[27][28][29][30] Existing machine learning methods like convolutional neural networks (CNNs) have shown promise as a segmentation tool across a variety of modalities but require a large volume of high-quality annotated data, as observed in previous studies centered around sinus segmentation. [31][32][33] True clinical applicability demands a more data-efficient alternative. 34 Subspace approximation with augmented kernels (Saak) is a novel transformation that offers a fully reversible and data-efficient means of feature extraction.…”

Section: Introductionmentioning

confidence: 99%

“…Existing machine learning methods like convolutional neural networks (CNNs) have shown promise as a segmentation tool across a variety of modalities but require a large volume of high-quality annotated data, as observed in previous studies centered around sinus segmentation. 31 32 33 True clinical applicability demands a more data-efficient alternative. 34…”

Section: Introductionmentioning

confidence: 99%

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Development of a Machine Learning–Enabled Virtual Reality Tool for Preoperative Planning of Functional Endoscopic Sinus Surgery

Gudapati,

Chen,

Meyer

et al. 2024

J Neurol Surg Rep

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Objectives: Virtual reality (VR) is an increasingly valuable teaching tool, but current simulators are not typically clinically scalable due to their reliance on inefficient manual segmentation. The objective of this project was to leverage a high-throughput and accurate machine learning method to automate data preparation for a patient-specific VR simulator used to explore preoperative sinus anatomy Methods: An endoscopic VR simulator was designed in Unity to enable interactive exploration of sinus anatomy. The Saak transform, a data-efficient machine learning method, was adapted to accurately segment sinus CT scans using minimal training data, and the resulting data was reconstructed into 3D patient-specific models that could be explored in the simulator. Results: Using minimal training data, the Saak transform-based machine learning method offers accurate soft-tissue segmentation. When explored with an endoscope in the VR simulator, the anatomical models generated by the algorithm accurately capture key sinus structures and showcase patient-specific variability in anatomy. Conclusions: By offering an automatic means of preparing VR models from a patient’s raw CT scans, this pipeline takes a key step towards clinical scalability. In addition to preoperative planning, this system also enables virtual endoscopy—a tool that is particularly useful in the COVID-19 era. As VR technology inevitably continues to develop, such a foundation will help ensure that future innovations remain clinically accessible.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a Machine Learning–Enabled Virtual Reality Tool for Preoperative Planning of Functional Endoscopic Sinus Surgery

Gudapati,

Chen,

Meyer

et al. 2024

J Neurol Surg Rep

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“…The degree of pneumatization of the sphenoid sinus ranges from absence or poor (conchal type) to hyperextension beyond the basal surface of the sphenoid bone, with potential involvement of the anterior and posterior clinoid processes, the lesser and greater sphenoid wings, the pterygoid process and plates, and into the clivus 9 . Additionally, the deep anatomical location of the sphenoid sinus within the sphenoid body is well protected from traumatic degradation resulting from an external cause 6 , 10 .…”

Section: Introductionmentioning

confidence: 99%

“…The analysis with regards to the sphenoid sinus in many studies described variation and highlighted their findings in their respective populations. Most of these results, however, are based on the Western population [6,[10][11][12][13][14]. The authors aim to study the possibility of variation among the race, and gender in the SEA population, utilizing a volumetric measurement of the sphenoid sinus.…”

Section: Introductionmentioning

confidence: 99%

Volumetric evaluation of the sphenoid sinus among different races in the Southeast Asian (SEA) population: a computerized tomography study

Tuang¹,

Zahedi²,

Husain³

et al. 2023

Int. J. Med. Sci.

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Introduction: The fundament of forensic science lies in identifying a body. The morphological complexity of the paranasal sinus (PNS), which varies greatly amongst individual, possess a discriminatory value that potentially contributes to the radiological identification. The sphenoid bone represents the keystone of the skull and forms part of the cranial vault. It is intimately associated with vital neurovascular structures. The sphenoid sinus, located within the body of the sphenoid bone, has variable morphology. The sphenoid septum's inconsistent position and the degree, as well as the direction disparities of sinus pneumatization, have indeed accorded it a unique structure in providing invaluable information in forensic personnel identification. Additionally, the sphenoid sinus is situated deep within the sphenoid bone. Therefore, it is well protected from traumatic degradation from external causes and can be potentially utilized in forensic studies. The authors aim to study the possibility of variation among the race, and gender in the Southeast Asian (SEA) population, using volumetric measurements of the sphenoid sinus. Materials and methods: This is a retrospective cross-sectional analysis of computerized tomographic (CT) imaging of the PNS of 304 patients (167 males, 137 females) in a single centre. The volume of the sphenoid sinus was reconstructed and measured using commercial real-time segmentation software. Result: The total volume of sphenoid sinus of male gender had shown to be larger, 12.22 (4.93 - 21.09) cm 3 compared to the counterpart of 10.19 (3.75 - 18.72) cm 3 ( p = .0090). The Chinese possessed a larger total sphenoid sinus volume, 12.96 (4.62 - 22.21) cm 3 ) than the Malays, 10.68 (4.13 - 19.25) cm 3 ( p = .0057). No correlation was identified between the age and volume of the sinus (cc= -.026, p = .6559). Conclusion: The sphenoid sinus volume in males was found to be larger than those of females. It was also shown that race influences sinus volume. Volumetric analysis of the sphenoid sinus can potentially be utilized in gender and race determination. The current study provided normative data on the sphenoid sinus volume in the SEA region, which can be helpful for future studies.

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Efficient Embedding Network for 3D Brain Tumor Segmentation

Messaoudi

Belaid

Allaoui

et al. 2021

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization

Cited by 17 publications

References 29 publications

Development of a Machine Learning–Enabled Virtual Reality Tool for Preoperative Planning of Functional Endoscopic Sinus Surgery

Development of a Machine Learning–Enabled Virtual Reality Tool for Preoperative Planning of Functional Endoscopic Sinus Surgery

Volumetric evaluation of the sphenoid sinus among different races in the Southeast Asian (SEA) population: a computerized tomography study

Efficient Embedding Network for 3D Brain Tumor Segmentation

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