The do's and don'ts of reinforcement learning for tractographyDownload PDF

21 Apr 2022, 15:59 (edited 04 Jun 2022)MIDL 2022 Short PapersReaders: Everyone
  • Keywords: Tractography, Deep reinforcement learning
  • TL;DR: We investigate the Track-to-Learn framework by varying its components to assess what to do and not to do to obtain accurate white matter reconstructions.
  • Abstract: Tractography is the process of virtually reconstructing the white matter structure of the brain in a non-invasive manner. To tackle the various known problems of tractography, deep learning has been proposed, but the lack of well curated diverse datasets makes neural networks incapable of generalizing well on unseen data. Recently, deep reinforcement learning (RL) has been shown to effectively learn the tractography procedure without reference streamlines. While the performances reported were competitive, the proposed framework is complex and little is known on the role and impact of its multiple parts. In this work, we thoroughly explore the different components of the proposed framework through seven experiments on two datasets and shed light on their impact. Our goal is to guide researchers eager to explore the possibilities of deep RL for tractography by exposing what works and what does not work with this category of approach. We find that directionality is crucial for the agents to learn the tracking procedure and that the input signal and the seeding strategy offer a trade-offs in connectivity vs. volume.
  • Registration: I acknowledge that acceptance of this work at MIDL requires at least one of the authors to register and present the work during the conference.
  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
  • Paper Type: novel methodological ideas without extensive validation
  • Primary Subject Area: Validation Study
  • Secondary Subject Area: Image Synthesis
  • Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
1 Reply

Loading