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Multiorgan structures detection using deep convolutional neural networks.

Jorge Onieva Onieva1, Germán González Serrano1, Thomas P Young1

  • 1Applied Chest Imaging Laboratory, Dept. of Radiology, Brigham and Women's Hospital, 1249 Boylston St, Boston, MA USA.

Proceedings of Spie--The International Society for Optical Engineering
|August 21, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a two-stage deep convolutional neural network (DCNN) to locate anatomical structures in medical images. The method accurately detects and outlines structures in Computed Tomography (CT) scans, proving generalizable for various anatomical targets.

Keywords:
computed tomographyconvolutional neural networkdeep learningorgan detector

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Radiology

Background:

  • Accurate initialization is crucial for medical image analysis algorithms.
  • Clinical workflows often rely on precise localization of anatomical landmarks.
  • Obtaining labeled medical images for training is a significant challenge.

Purpose of the Study:

  • To develop a robust method for detecting and localizing anatomical structures in 2D medical images.
  • To address the challenge of limited labeled data in medical imaging.
  • To create a generalizable system for identifying diverse anatomical structures.

Main Methods:

  • A two-stage deep convolutional neural network (DCNN) approach was implemented.
  • The first stage detects the optimal image slice containing the anatomical structure.
  • The second stage performs 2D bounding box regression to encompass the identified structure.

Main Results:

  • The system was trained and tested on 504 labeled Computed Tomography (CT) scans, analyzing 57 anatomical structures across axial, sagittal, and coronal planes.
  • The DCNN method demonstrated promising and consistent results compared to the Viola Jones algorithm and human expert inter-rater error.
  • The architecture showed high generalizability across a wide range of anatomical structures.

Conclusions:

  • The proposed two-stage DCNN system effectively detects and localizes anatomical structures in medical images.
  • The method offers a generalizable solution for anatomical structure localization, even with limited training data.
  • This approach has the potential to improve various medical image analysis tasks and clinical workflows.