Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A parallel resnet-transformer and cross-modal attention network for identification of rice blast resistance using UAV-based visible-infrared imagery.

Plant phenomics (Washington, D.C.)·2026

Same author

Research on Object Detection in Cluttered Hospital Corridor Scenes with CSAWOA-YOLOv8.

Biomimetics (Basel, Switzerland)·2026

Same author

NADPH Dehydrogenase Gene Regulates Energy Distribution and Fatty Acid Metabolism During Fruiting Body Formation in the Filamentous Fungus Podospora anserina.

Microbial biotechnology·2026

Same author

Research on Reinforcement Learning-Based Autonomous Navigation and Obstacle Avoidance Methods for AGVs in Unknown Hospital Environments.

Sensors (Basel, Switzerland)·2026

Same author

Natural-language-processing and safety-engineering-based fault identification technique for electrochemical ESSs.

Innovation (Cambridge (Mass.))·2026

Same author

CMPK2 restricts <i>Mycobacterium tuberculosis</i> replication and regulates macrophage gene expression.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Aug 25, 2025

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

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

Published on: November 30, 2022

Data augmentation based on multiple oversampling fusion for medical image segmentation.

Liangsheng Wu^1,2,3, Jiajun Zhuang², Weizhao Chen¹

¹Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou, China.

|October 18, 2022

Summary

This summary is machine-generated.

Medical image segmentation models require extensive annotated data, which is challenging to acquire. This study introduces a novel data augmentation technique to improve the segmentation of small lesions, enhancing model performance.

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Published on: June 3, 2018

Related Experiment Videos

Last Updated: Aug 25, 2025

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

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

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Published on: June 3, 2018

Area of Science:

Medical Imaging
Deep Learning
Computer Vision

Background:

High-performance medical image segmentation relies on large annotated datasets.
Acquiring sufficient annotated medical images is difficult.
Small tissue lesions exacerbate class imbalance in segmentation tasks.

Purpose of the Study:

To propose a multidimensional data augmentation method for medical image segmentation.
To address the class imbalance problem caused by small tissue lesions.
To improve the performance of deep learning models in segmenting small lesions.

Main Methods:

Implemented a data augmentation strategy combining affine transformation and random oversampling.
Utilized class weight balancing with a weighted cross-entropy loss function.
Evaluated four deep neural network models (Mask-RCNN, U-Net, SegNet, DeepLabv3+) on LUNA16 and LiTS17 datasets.

Main Results:

The proposed data augmentation strategy significantly improved small tissue segmentation performance across all tested architectures.
Mask-RCNN achieved the best pixelwise segmentation performance for pulmonary nodules (DSC: 0.829) and liver tumors (DSC: 0.879).
The results are comparable to state-of-the-art methods.

Conclusions:

The multidimensional data augmentation method effectively enhances medical image segmentation for small lesions.
Class imbalance in medical image segmentation can be mitigated using weighted loss functions and oversampling techniques.
The Mask-RCNN model, combined with the proposed augmentation, demonstrates superior performance in segmenting small pulmonary nodules and liver tumors.