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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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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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Imaging Studies III: Computed Tomography01:27

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Classification of Bones01:18

Classification of Bones

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
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Related Experiment Video

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Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
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Automated Bone Tumor Segmentation and Classification as Benign or Malignant Using Computed Tomographic Imaging.

Ilkay Yildiz Potter1, Diana Yeritsyan2, Sarah Mahar2

  • 1BioSensics LLC, 57 Chapel Street, Newton, MA, 02458, USA. ilkay.yildiz@biosensics.com.

Journal of Digital Imaging
|January 10, 2023
PubMed
Summary

This study introduces an automated deep learning method for bone tumor segmentation and classification using computed tomography (CT) scans. The AI model aids clinicians in identifying malignant tumors, potentially reducing the need for biopsies.

Keywords:
Bone tumorClassificationComputed tomographyDeep learningSegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate bone tumor diagnosis is crucial for treatment planning.
  • Distinguishing between benign and malignant bone lesions can be challenging.
  • Current diagnostic methods may involve invasive procedures like biopsies.

Purpose of the Study:

  • To develop and evaluate an automated system for bone tumor segmentation and classification using computed tomography (CT) imaging and machine learning.
  • To assist clinicians in determining the necessity of a biopsy for bone lesions.
  • To establish a deep learning framework for analyzing bone tumor characteristics from CT scans.

Main Methods:

  • A retrospective study utilizing 84 femur CT scans with confirmed histologic diagnoses (71% malignant).
  • A deep learning architecture was employed to predict segmentation masks and classify lesions as benign or malignant from DICOM slices.
  • Automated classification was finalized using majority voting, with statistical analysis via fivefold cross-validation.

Main Results:

  • The automated system achieved 75% specificity and 79% sensitivity for classification, despite dataset imbalance.
  • Average segmentation performance reached a 56% Dice score, with peak performance of 80% on individual slices.
  • The model demonstrated capability in learning typical tumor characteristics through visual analysis.

Conclusions:

  • The developed deep learning approach represents a significant step towards automated bone tumor analysis from CT imaging.
  • The system shows comparable performance to existing models using other imaging modalities.
  • This automated method shows promise in supporting clinical decision-making regarding bone tumor biopsies.