research-article

Fully Convolutional One-Stage Circular Object Detector on Medical Images

Authors:

Wenxin ZhaoAuthors Info & Claims

ICAIP '20: Proceedings of the 4th International Conference on Advances in Image Processing

Pages 21 - 26

https://doi.org/10.1145/3441250.3441269

Published: 29 May 2021 Publication History

Abstract

In an endoscopic thyroidectomy, parathyroid injury can lead to hypocalcemia for life. It is of great help to assist surgeons to complete endoscopic thyroidectomy without damaging the parathyroid. Considering that the shape of the parathyroid is usually round, we replaced the rectangle bounding box with the circular bounding box, and propose a fully convolutional one-stage circular object detector (FCOSC) based on FCOS. The loss and optimization are improved to adapt to the calculation of the circular bounding box. FCOSC achieved 37.9% in AP with single-model testing on the parathyroid dataset, which was 5% higher than FCOS. Considering that our data is strictly confidential, we also evaluated the accuracy of our method in the BCCD dataset and achieved 70.4% in AP, comparing to 68.4% with a rectangle bounding box. Experiments show that the proposed FCOSC framework can obtain expected results on parathyroid detection and be used in another metical circular object detection task.

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https://doi.org/10.1109/TIM.2024.3502719
Ding GZhao XPeng CLi LGuo JLi DJiang X(2023)Anchor-Free Feature Aggregation Network for Instrument Detection in Endoscopic SurgeryIEEE Access10.1109/ACCESS.2023.325040011(29464-29473)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3250400
Carrillo-Perez BBarnes SStephan M(2022)Ship Segmentation and Georeferencing from Static Oblique View ImagesSensors10.3390/s2207271322:7(2713)Online publication date: 1-Apr-2022
https://doi.org/10.3390/s22072713

Index Terms

Fully Convolutional One-Stage Circular Object Detector on Medical Images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

ICAIP '20: Proceedings of the 4th International Conference on Advances in Image Processing

November 2020

191 pages

ISBN:9781450388368

DOI:10.1145/3441250

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 29 May 2021

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the Joint Funds for the innovation of science and Technology, Fujian province
Fujian Provincial Health Technology Project

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ICAIP 2020

ICAIP 2020: 2020 4th International Conference on Advances in Image Processing

November 13 - 15, 2020

Chengdu, China

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Cited By

Jiao LLiu HLiang ZChen PWang RLiu K(2025)An Anchor-Free Refining Feature Pyramid Network for Dense and Multioriented Wheat Spikes Detection Under UAVIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.350271974(1-14)Online publication date: 2025
https://doi.org/10.1109/TIM.2024.3502719
Ding GZhao XPeng CLi LGuo JLi DJiang X(2023)Anchor-Free Feature Aggregation Network for Instrument Detection in Endoscopic SurgeryIEEE Access10.1109/ACCESS.2023.325040011(29464-29473)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3250400
Carrillo-Perez BBarnes SStephan M(2022)Ship Segmentation and Georeferencing from Static Oblique View ImagesSensors10.3390/s2207271322:7(2713)Online publication date: 1-Apr-2022
https://doi.org/10.3390/s22072713

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