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Learning soft tissue behavior of organs for surgical navigation with convolutional neural networks

  • Micha PfeifferEmail author
  • Carina Riediger
  • Jürgen Weitz
  • Stefanie Speidel
Original Article
  • 7 Downloads

Abstract

Purpose

In surgical navigation, pre-operative organ models are presented to surgeons during the intervention to help them in efficiently finding their target. In the case of soft tissue, these models need to be deformed and adapted to the current situation by using intra-operative sensor data. A promising method to realize this are real-time capable biomechanical models.

Methods

We train a fully convolutional neural network to estimate a displacement field of all points inside an organ when given only the displacement of a part of the organ’s surface. The network trains on entirely synthetic data of random organ-like meshes, which allows us to use much more data than is otherwise available. The input and output data are discretized into a regular grid, allowing us to fully utilize the capabilities of convolutional operators and to train and infer in a highly parallelized manner.

Results

The system is evaluated on in-silico liver models, phantom liver data and human in-vivo breathing data. We test the performance with varying material parameters, organ shapes and amount of visible surface. Even though the network is only trained on synthetic data, it adapts well to the various cases and gives a good estimation of the internal organ displacement. The inference runs at over 50 frames per second.

Conclusion

We present a novel method for training a data-driven, real-time capable deformation model. The accuracy is comparable to other registration methods, it adapts very well to previously unseen organs and does not need to be re-trained for every patient. The high inferring speed makes this method useful for many applications such as surgical navigation and real-time simulation.

Keywords

Surgical navigation Soft tissue Biomechanical model Organ deformation Convolutional neural network 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.National Center for Tumor Diseases (NCT), Partner Site DresdenDresdenGermany
  2. 2.Department for Visceral, Thoracic and Vascular Surgery, University HospitalTechnical University DresdenDresdenGermany

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