@inbook{ae48b508ce2e49599a195d223760ea21,
title = "DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images",
abstract = "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.",
keywords = "CNNs, Deep learning, Interactive segmentation",
author = "Andres Diaz-Pinto and Pritesh Mehta and Sachidanand Alle and Muhammad Asad and Richard Brown and Vishwesh Nath and Alvin Ihsani and Michela Antonelli and Daniel Palkovics and Csaba Pinter and Ron Alkalay and Steve Pieper and Roth, {Holger R.} and Daguang Xu and Prerna Dogra and Tom Vercauteren and Andrew Feng and Abood Quraini and Sebastien Ourselin and Cardoso, {M. Jorge}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 2nd MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 ; Conference date: 22-09-2022 Through 22-09-2022",
year = "2022",
doi = "10.1007/978-3-031-17027-0_2",
language = "English",
isbn = "9783031170263",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "11--21",
editor = "Nguyen, {Hien V.} and Huang, {Sharon X.} and Yuan Xue",
booktitle = "Data Augmentation, Labelling, and Imperfections - 2nd MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Proceedings",
address = "Germany",
}