Objective
The blood flow in lymph nodes reflects important pathological features. However, most
intelligent diagnosis based on contrast-enhanced ultrasound (CEUS) video focuses only
on CEUS images, ignoring the process of extracting blood flow information. In the
work described here, a parametric imaging method for describing blood perfusion pattern
was proposed and a multimodal network (LN-Net) to predict lymph node metastasis was
designed.
Methods
First, the commercially available artificial intelligence object detection model YOLOv5
was improved to detect the lymph node region. Then the correlation and inflection
point matching algorithms were combined to calculate the parameters of the perfusion
pattern. Finally, the Inception-V3 architecture was used to extract the image features
of each modality, with the blood perfusion pattern taken as the guiding factor in
fusing the features with CEUS by sub-network weighting.
Discussion
The average precision of the improved YOLOv5s algorithm compared with baseline was
improved by 5.8%. LN-Net predicted lymph node metastasis with 84.9% accuracy, 83.7%
precision and 80.3% recall. Compared with the model without blood flow feature guidance,
accuracy was improved by 2.6%. The intelligent diagnosis method has good clinical
interpretability.
Conclusion
A static parametric imaging map could describe a dynamic blood flow perfusion pattern,
and as a guiding factor, it could improve the classification ability of the model
with respect to lymph node metastasis.
Keywords
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Article info
Publication history
Published online: February 16, 2023
Accepted:
January 14,
2023
Received in revised form:
January 10,
2023
Received:
September 17,
2022
Identification
Copyright
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