Robust deep learning-based semantic organ segmentation in hyperspectral images

@article{Seidlitz2022RobustDL,
  title={Robust deep learning-based semantic organ segmentation in hyperspectral images},
  author={Silvia Seidlitz and Jan Sellner and Jan Odenthal and Berkin {\"O}zdemir and Alexander Studier-Fischer and Samuel Kn{\"o}dler and Leonardo A. Ayala and Tim J. Adler and Hannes G Kenngott and Minu Dietlinde Tizabi and Martin Wagner and Felix Nickel and Beat Peter M{\"u}ller-Stich and Lena Maier-Hein},
  journal={Medical image analysis},
  year={2022},
  volume={80},
  pages={
          102488
        }
}

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References

SHOWING 1-10 OF 90 REFERENCES

Tumor Semantic Segmentation in Hyperspectral Images using Deep Learning

TLDR
This work proposes using channels selection with U-Net deep neural network for tumor segmentation in hyperspectral images and achieves better results than pixel-level spectral and structural approaches in a clinical data set with tongue squamous cell carcinoma.

Tongue Tumor Detection in Hyperspectral Images Using Deep Learning Semantic Segmentation

TLDR
This is the first work using deep learning semantic segmentation for tumor detection in HSI data using channel selection, and accounting for more spatial tissue context, and global comparison between the prediction map, and the annotation per sample.

Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging

TLDR
A novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery.

Efficient Deep Auto-Encoder Learning for the Classification of Hyperspectral Images

  • Atif MugheesL. Tao
  • Environmental Science, Computer Science
    2016 International Conference on Virtual Reality and Visualization (ICVRV)
  • 2016
TLDR
Experimental results with widely-used hyperspectral data confirms that the new spectral and spatial classification approach is able to improve results significantly in terms of classification accuracies.

CRF learning with CNN features for hyperspectral image segmentation

TLDR
This paper attempts to combine the properties of CNN and Conditional Random Field, using a mean-field approximation algorithm for CRF inference and formulated with Gaussian pairwise potentials as Recurrent Neural Network.

Trends in Deep Learning for Medical Hyperspectral Image Analysis

TLDR
This work aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery by reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis ofmedical hyperspectrals imaging.

A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification

TLDR
The experimental results obtained with three public HSI datasets show that the SSDGL has powerful performance in insufficient and imbalanced sample problems and is superior to other state-of-the-art methods.

Spectral organ fingerprints for intraoperative tissue classification with hyperspectral imaging

TLDR
It is concluded that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decision making and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

TLDR
This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor.
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