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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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Related Experiment Video

Updated: Jun 30, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

LEARNABLE HIERARCHICAL VISUAL CONTEXTS FOR TUMOR SEGMENTATION IN COMPUTED TOMOGRAPHY IMAGES.

Jue Jiang1, Harini Veeraraghavan1

  • 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, 10065 NY, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|June 29, 2026
PubMed
Summary

LeVal improves automated tumor segmentation on CT scans using learnable visual query contexts. This deep learning method enhances radiotherapy accuracy by better distinguishing tumors from healthy tissues.

Keywords:
Few-shot learningLearnable context tokensPretrainingTumor segmentationVision transformer

Related Experiment Videos

Last Updated: Jun 30, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiotherapy

Background:

  • Automated tumor segmentation on CT scans is crucial for radiotherapy but remains challenging due to tumor variability.
  • Deep learning (DL) methods struggle with diffuse boundaries and appearance variations in tumors.

Purpose of the Study:

  • To introduce LeVal, a novel deep learning approach for enhanced automated tumor segmentation on CT scans.
  • To improve the accuracy and discriminability of tumor segmentation in radiotherapy applications.

Main Methods:

  • LeVal utilizes learnable visual query contexts (semantic and task-specific tokens) with a 3D Swin transformer encoder.
  • A two-stage pretraining strategy involving self-supervised learning (SSL) on unlabeled CTs and supervised pretraining for multi-organ segmentation was employed.
  • Task queries are refined via cross-attention with semantic contexts to modulate the decoder output for segmentation.

Main Results:

  • LeVal consistently outperformed existing methods across four public datasets (pancreas, colon, adrenal, head-and-neck cancers).
  • The method demonstrated improved embedding separation between tumor and healthy tissues, indicating enhanced discriminability.
  • The approach refines attention towards tumor-relevant regions for more precise segmentation.

Conclusions:

  • LeVal offers a significant advancement in automated tumor segmentation for radiotherapy.
  • The proposed method enhances the accuracy and robustness of deep learning models in medical image analysis.
  • Availability of code and model checkpoints will facilitate further research and application.