• Corpus ID: 229363724

CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80

@article{Hong2020CholecSeg8kAS,
  title={CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80},
  author={W.-Y. Hong and Chang-Lung Kao and Y.-H. Kuo and J.-R. Wang and Wanxing Chang and C.-S. Shih},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.12453}
}
Computer-assisted surgery has been developed to enhance surgery correctness and safety. However, the researchers and engineers suffer from limited annotated data to develop and train better algorithms. Consequently, the development of fundamental algorithms such as Simultaneous Localization and Mapping (SLAM) are limited. This article elaborates the efforts of preparing the dataset for semantic segmentation, which is the foundation of many computer-assisted surgery mechanisms. Based on the… 

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References

SHOWING 1-3 OF 3 REFERENCES

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

This paper proposes a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information.

Content-based processing and analysis of endoscopic images and videos: A survey

This survey aims to introduce this research field to a broader audience in the Multimedia community to stimulate further research, to describe domain-specific characteristics of endoscopic videos that need to be addressed in a pre-processing step, and to systematically bring together the very diverse research results for the first time.

Efficient Linear Algorithm for Magnetic Localization and Orientation in Capsule Endoscopy

  • Chao HuM. MengM. Mandal
  • Computer Science
    2005 IEEE Engineering in Medicine and Biology 27th Annual Conference
  • 2005
A linear algorithm is proposed to provide faster, more reliable computation, compared to the nonlinear algorithms, and shows that satisfactory localization and orientation accuracy can be achieved using a sensor array with enough number of 3-axis sensors.