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Mass Analyzers: Overview01:13

Mass Analyzers: Overview

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The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
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Mass Analyzers: Common Types01:19

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The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
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Related Experiment Video

Updated: May 2, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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Rethinking data imbalance in class incremental surgical instrument segmentation.

Shifang Zhao1, Long Bai2, Kun Yuan3

  • 1Department of Electronic Engineering, The Chinese University of Hong Kong (CUHK), Hong Kong.

Medical Image Analysis
|July 30, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces SurgCSS, a new framework to improve surgical instrument segmentation by addressing data imbalance in continual learning. It enhances model performance on incremental tasks, preventing forgetting and bias for better clinical application.

Keywords:
Continual learningData imbalanceData synthesisInstrument segmentationRobotic surgery

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Area of Science:

  • Computer Vision
  • Medical Imaging
  • Machine Learning

Background:

  • Surgical instrument segmentation faces challenges with evolving instrument types and catastrophic forgetting in neural networks.
  • Existing continual learning methods struggle with data imbalance in surgical scenarios, leading to biased segmentation heads and poor performance.

Purpose of the Study:

  • To propose SurgCSS, a novel plug-and-play continual semantic segmentation (CSS) framework designed for surgical instrument segmentation under data imbalance.
  • To address class imbalance between new/old data and within data from the same time point, which often biases CSS models.

Main Methods:

  • SurgCSS generates realistic surgical backgrounds via inpainting and blends foregrounds class-awarely to balance data distribution.
  • Introduces Class Desensitization Loss using contrastive learning to mitigate edge biases from data imbalance.
  • Dynamically fuses old and new model weights for an optimal trade-off between biased and unbiased parameters.

Main Results:

  • Extensive experiments on a new benchmark integrating four public datasets (EndoVis 2017/2018, CholecSeg8k, SAR-RAPR50) demonstrate significant performance improvements.
  • The proposed framework effectively overcomes data imbalance issues in surgical instrument CSS.
  • SurgCSS achieves substantial performance gains over existing baselines.

Conclusions:

  • SurgCSS offers an effective solution for surgical instrument segmentation in continual learning settings with data imbalance.
  • The framework shows excellent potential for real-world clinical applications.
  • The study introduces a new benchmark to facilitate research on surgical instrument CSS under data imbalance.