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Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks.

Yuankai Huo1, Zhoubing Xu1, Shunxing Bao2

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|June 12, 2018
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Summary
This summary is machine-generated.

A new Splenomegaly Segmentation Network (SSNet) accurately detects enlarged spleens in MRI scans. This deep learning method, using conditional generative adversarial networks, significantly reduces errors in spleen volume estimation for diagnosing splenomegaly.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Automated spleen volume estimation using image segmentation aids in detecting splenomegaly on MRI scans.
  • Deep Convolutional Neural Networks (DCNNs) show promise for abdominal organ segmentation but struggle with spleen size and shape variations, leading to labeling errors.
  • Existing DCNN methods often produce false positives and false negatives in spleen segmentation.

Purpose of the Study:

  • To introduce the Splenomegaly Segmentation Network (SSNet), a novel deep learning model designed to improve spleen segmentation accuracy, particularly for enlarged spleens.
  • To address the challenge of spatial variations in spleen size and shape that affect DCNN-based segmentation.
  • To enhance the reliability of spleen volume estimation for splenomegaly detection.

Main Methods:

  • SSNet was developed using an image-to-image conditional generative adversarial network (cGAN) framework.
  • A Global Convolutional Network (GCN) served as the generator to minimize false negatives.
  • A Markovian discriminator (PatchGAN) was employed as the discriminator to reduce false positives.
  • The network was trained and validated on a dataset of 3D MRI scans (T1 and T2 weighted) from patients with splenomegaly.

Main Results:

  • SSNet achieved a mean Dice coefficient of 0.9260.
  • The median Dice coefficient obtained with SSNet was 0.9262.
  • The model demonstrated high accuracy in segmenting spleen volumes on independently tested MRI data from patients with splenomegaly.

Conclusions:

  • SSNet effectively segments spleens in MRI scans, even in cases of splenomegaly with significant spatial variations.
  • The proposed network offers a robust solution for accurate spleen volume estimation, improving splenomegaly detection.
  • SSNet shows potential for clinical application in the diagnosis and monitoring of conditions involving spleen enlargement.