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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.

Teng Li¹, Yanzhe Xu¹, Teresa Wu¹

¹School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA.

Bioengineering (Basel, Switzerland)

|December 23, 2023

Summary

BlobCUT, a novel 3D small blob detector, enhances medical imaging by accurately segmenting tiny objects. This contrastive unpaired translation model offers superior performance and improved training efficiency for blob identification.

Keywords:

Hessian analysis blob detection contrastive learning glomeruli segmentation imaging biomarker

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Area of Science:

Medical Imaging
Biomarker Discovery
Computational Biology

Background:

Medical imaging-based biomarkers from small objects like cell nuclei are vital.
Detecting and segmenting these small objects (blobs) presents significant challenges.

Purpose of the Study:

To introduce BlobCUT, a novel 3D small blob detector.
To improve the accuracy and efficiency of small object segmentation in medical imaging.

Main Methods:

BlobCUT utilizes a contrastive unpaired image-to-image (I2I) translation approach.
Incorporates a blob synthesis module for generating synthetic 3D blobs and masks.
Employs convexity consistency and intensity distribution consistency constraints during training.

Main Results:

BlobCUT demonstrates superior performance in segmenting 3D blobs compared to state-of-the-art methods.
Achieves significant training efficiency, requiring only 56.6% of the training time of BlobDetGAN.
Effectively functions as a denoising process for blob identification.

Conclusions:

BlobCUT is an effective tool for accurate 3D small blob segmentation in medical imaging.
The model offers substantial improvements in training efficiency, making it a practical solution.