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Related Concept Videos

Cranial Bones: Lateral View01:27

Cranial Bones: Lateral View

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The lateral view of the cranium is dominated by temporal, sphenoid, and ethmoid bones.
The temporal bone forms the lower lateral side of the skull. The temporal bone is subdivided into several regions. The flattened upper portion is the squamous portion of the temporal bone. Below this area and projecting anteriorly is the zygomatic process of the temporal bone, which forms the posterior portion of the zygomatic arch. Posteriorly is the mastoid portion of the temporal bone. Projecting...
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The cranium (skull) is the skeletal structure of the head that supports the face and protects the brain. It is subdivided into the facial bones and the brain case, or cranial vault. The facial bones underlie the facial structures, form the nasal cavity, enclose the eyeballs, and support the teeth of the upper and lower jaws.
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Human identification performed with skull's sphenoid sinus based on deep learning.

Hanjie Wen1, Wei Wu2, Fei Fan2

  • 1College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China.

International Journal of Legal Medicine
|January 13, 2022
PubMed
Summary

A new deep learning method uses 3D sphenoid sinus models for rapid human identification in forensic cases. This advanced technique achieves high accuracy, aiding investigations with fast and reliable results.

Keywords:
3D sphenoid sinusCTDeeping learningHuman identification

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Area of Science:

  • Forensic Science
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Human identification is crucial in forensic investigations.
  • Traditional methods can be time-consuming.
  • 3D sphenoid sinus models offer unique anatomical features.

Purpose of the Study:

  • To develop and evaluate a deep learning framework for human identification using 3D sphenoid sinus models.
  • To assess the accuracy and speed of the proposed identification method.

Main Methods:

  • A retrospective study utilizing 1475 noncontrast thin-slice CT scans from 732 patients.
  • Development of a customized convolutional neural network (MVSS-Net) for sphenoid sinus modeling and recognition.
  • Training and testing the deep learning model on segmented sphenoid sinus data.

Main Results:

  • The deep learning framework achieved Rank 1 accuracy of 93.94% and Rank 5 accuracy of 99.24% on the test set.
  • Identification process completed within 55 seconds, demonstrating high efficiency.
  • Manual verification of framework results yielded 100% accuracy in the test set.

Conclusions:

  • Customized deep learning on 3D sphenoid sinus models provides a reliable and fast method for human identification.
  • This approach can significantly assist forensic radiologists in improving identification accuracy.
  • The framework effectively excludes low-similarity models and supports visual comparisons.