Image:
Eirik Østmo / Torger Grytå

VI seminar #23 – nnU-Net

The program will be available shortly. Please check back later.

nnU-Net: a self-configuring method for deeplearning-based biomedical image segmentation

Presenter: Fabian Isensee, German Cancer Research Center (Deutsches Krebsforschungszentrum) DKFZ

Abstract: Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to abroad audience by requiring neither expert knowledge nor computing resources beyond standard network training.

In compliance with GDPR consent requirements, presentations given in a Visual Intelligence context maybe recorded with the consent of the speaker. All recordings are edited to remove all faces, names and voices of other participants. Questions and comments by the audience will hence be removed and will not appear in the recording.  With the freely given consent from the speaker, recorded presentation may be posted on the Visual Intelligence YouTube channel.

This seminar is open for members of the consortium. If you want to participate as a guest please sign up.

Sign up here