DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D
  Medical Images
Andres Diaz-Pinto Pritesh Mehta Sachidanand Alle Muhammad Asad Richard Brown Vishwesh Nath Alvin Ihsani Michela Antonelli Daniel Palkovics Csaba Pinter Ron Alkalay Steve Pieper Holger R. Roth Daguang Xu Prerna Dogra Tom Vercauteren Andrew Feng Abood Quraini Sebastien Ourselin M. Jorge Cardoso
Automatic segmentation of medical images is a key step for diagnostic and
interventional tasks. However, achieving this requires large amounts of
annotated volumes, which can be tedious and time-consuming task for expert
annotators. In this paper, we introduce DeepEdit, a deep learning-based method
for volumetric medical image annotation, that allows automatic and
semi-automatic segmentation, and click-based refinement. DeepEdit combines the
power of two methods: a non-interactive (i.e. automatic segmentation using
nnU-Net, UNET or UNETR) and an interactive segmentation method (i.e. DeepGrow),
into a single deep learning model. It allows easy integration of
uncertainty-based ranking strategies (i.e. aleatoric and epistemic uncertainty
computation) and active learning. We propose and implement a method for
training DeepEdit by using standard training combined with user interaction
simulation. Once trained, DeepEdit allows clinicians to quickly segment their
datasets by using the algorithm in auto segmentation mode or by providing
clicks via a user interface (i.e. 3D Slicer, OHIF). We show the value of
DeepEdit through evaluation on the PROSTATEx dataset for prostate/prostatic
lesions and the Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) dataset
for abdominal CT segmentation, using state-of-the-art network architectures as
baseline for comparison. DeepEdit could reduce the time and effort annotating
3D medical images compared to DeepGrow alone. Source code is available at
https://github.com/Project-MONAI/MONAILabel