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Related Experiment Video

Updated: Sep 22, 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

A pixel-level coarse-to-fine image segmentation labelling algorithm.

Jonghyeok Lee^1,2, Talha Ilyas^1,2, Hyungjun Jin^1,2

¹Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896, Republic of Korea.

Scientific Reports

|May 23, 2022

Summary

This summary is machine-generated.

This study introduces a new method for automated pixel-level segmentation labeling, significantly reducing manual labor. The approach uses coarse labels to generate precise fine labels, achieving high accuracy in agricultural and disease datasets.

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Last Updated: Sep 22, 2025

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

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Published on: August 13, 2014

Area of Science:

Computer Vision
Machine Learning
Agricultural Technology

Background:

Fine segmentation labeling is labor-intensive and time-consuming.
Existing methods often require extensive manual annotation.
Efficient and accurate segmentation is crucial for agricultural and medical image analysis.

Purpose of the Study:

To develop a novel method for efficient pixel-level fine segmentation labeling.
To reduce the manual labor required for creating detailed segmentation masks.
To validate the proposed method on diverse agricultural and disease datasets.

Main Methods:

Utilizes multiple, complementary coarse labels generated automatically and manually.
Employs supervised learning to construct a complete fine label from coarse inputs.
Primary manual label created using simple contours or bounding boxes.
Complementary coarse labels generated using existing algorithms.

Main Results:

Achieved 92% fine label Intersection over Union (IOU) on a new bean field dataset.
Reached 95% and 92% mean IOU on public CVPPP and CWFID agricultural datasets.
Obtained 81% mean IOU on a multi-category paprika disease dataset.

Conclusions:

The proposed method significantly reduces manual effort in fine segmentation labeling.
Demonstrates high accuracy and generalizability across different datasets and applications.
Offers a promising solution for efficient image segmentation in various fields.