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Imaging Studies I: Kidney, Ureter, and Bladder Studies

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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...

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Updated: Apr 30, 2026

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

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Published on: November 30, 2022

PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation.

Ruining Deng¹, Quan Liu¹, Can Cui¹

¹Vanderbilt University.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition

|March 21, 2025

Summary

This summary is machine-generated.

This study introduces panoramic renal pathology segmentation (PrPSeg), a novel method for segmenting kidney structures. PrPSeg integrates anatomical knowledge to improve disease diagnostics and clinical research.

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Area of Science:

Nephrology
Medical Imaging
Computational Pathology

Background:

Accurate renal pathology segmentation is vital for diagnostics and research.
Existing methods often neglect spatial relationships between kidney structures.
Understanding kidney anatomy across multiple levels (regions, units, cells) is complex.

Purpose of the Study:

To develop a novel segmentation approach for comprehensive panoramic renal pathology.
To integrate extensive kidney anatomical knowledge into the segmentation process.
To improve the accuracy of identifying and classifying renal structures.

Main Methods:

Introduction of a universal proposition learning approach named PrPSeg.
Design of a comprehensive universal proposition matrix for renal pathology.
Development of a token-based dynamic head single network architecture.
Implementation of an anatomy loss function to quantify inter-object relationships.

Main Results:

Successfully segmented panoramic structures within the kidney.
Demonstrated improved incorporation of classification and spatial relationships.
Showcased enhanced partial label image segmentation capabilities.
Validated the quantification of inter-object relationships through the anatomy loss function.

Conclusions:

PrPSeg offers a novel and effective method for renal pathology segmentation.
The approach enhances the integration of anatomical knowledge for improved diagnostics.
The proposed architecture and loss function advance the field of kidney image analysis.