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

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Published on: November 30, 2022

Robust deep learning-based semantic organ segmentation in hyperspectral images.

Silvia Seidlitz¹, Jan Sellner¹, Jan Odenthal²

¹Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany; Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany.

Medical Image Analysis

|June 6, 2022

Summary

This summary is machine-generated.

Hyperspectral imaging (HSI) enhances surgical organ segmentation accuracy over RGB data. Deep learning models benefit from increased spatial context, achieving performance comparable to human experts for autonomous robotic surgery.

Keywords:

Deep learning Hyperspectral imaging Open surgery Organ segmentation Semantic scene segmentation Surgical data science

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Area of Science:

Medical Imaging
Computer Vision
Surgical Robotics

Background:

Semantic image segmentation is crucial for surgical context-awareness and autonomous robotics.
Current research primarily uses RGB data from minimally invasive surgery, neglecting hyperspectral imaging (HSI) in open surgery.

Purpose of the Study:

Investigate HSI data representation for neural network-based organ segmentation in open surgery.
Compare HSI data against RGB and processed HSI data for segmentation performance.

Main Methods:

Utilized hyperspectral imaging (HSI) data from pigs in an open surgery setting.
Evaluated different spatial granularities (pixels, superpixels, patches, full images) for neural network input.
Conducted a validation study on 506 HSI images across 20 pigs, annotated with 19 classes.

Main Results:

Deep learning segmentation performance improved with increased spatial context across all modalities.
Unprocessed HSI data outperformed RGB and processed HSI data, especially with smaller input sizes.
Maximum performance (whole images) achieved a mean Dice Similarity Coefficient (DSC) of 0.90, matching inter-rater variability.

Conclusions:

Hyperspectral imaging (HSI) shows significant potential for fully-automatic surgical scene understanding.
HSI offers advantages over traditional imaging, including recovery of functional tissue information.
The study provides code and pre-trained models for advancing HSI in surgical applications.