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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Updated: May 7, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Defect-adaptive landmark detection in pelvis CT images via personalized structure-aware learning.

Xirui Zhao¹, Deqiang Xiao², Teng Zhang³

¹School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|January 1, 2026

Summary

We developed DADNet, a novel network for precise pelvic landmark detection in CT scans, even with bone defects. This defect-adaptive detection network improves accuracy for orthopedic preoperative planning.

Keywords:

CT image Defective pelvis Knowledge personalization Landmark detection

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Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

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Last Updated: May 7, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

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Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

Area of Science:

Medical Imaging
Computer Vision
Orthopedic Surgery

Background:

Accurate localization of pelvic anatomical landmarks is vital for orthopedic preoperative planning.
Existing automatic methods struggle with defective bone structures common in clinical cases.

Purpose of the Study:

To propose DADNet, a defect-adaptive detection network for accurate and robust landmark detection in defective pelvis CT images.
To incorporate personalized structural priors to enhance landmark detection performance.

Main Methods:

DADNet constructs a structure-aware soft prior map encoding landmark spatial distribution.
A patch-based context-aware network performs landmark regression guided by the prior map.
A bone-aware detection loss enhances robustness in defective regions, with dynamic weight adjustment.

Main Results:

DADNet achieved an average detection error of 1.252 ± 0.075 mm on severely defective pelvic CT cases.
The method significantly outperformed existing techniques on public and private datasets.
Demonstrated strong adaptability to anatomical variability and structural incompleteness.

Conclusions:

DADNet offers accurate and robust landmark detection in challenging clinical scenarios with pelvic bone defects.
The proposed framework shows promise for improving preoperative planning in orthopedic surgery.
Personalized structural priors and defect-adaptive strategies are key to enhanced performance.