CoChat
Sign Up
Cited by 130Medical Image Segmentation TechniquesAdvanced Neural Network ApplicationsMedical Imaging and Analysis

The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.

MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning

Open access
Luo, Xiangde, Wang, Guotai, Song, Tao, Zhang, Jingyang, Aertsen, Michael, Deprest, Jan, Ourselin, Sebastien, Vercauteren, Tom, Zhang, Shaoting·2021·Medical Image Analysis
DOI 10.1016/j.media.2021.102102PMC PMC7613452
Open this paper in CoChat — free account

Abstract

Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Networks (CNN) have achieved the state-of-the-art performance for automatic segmentation, they are often limited by the lack of clinically acceptable accuracy and robustness in complex cases. Therefore, interactive segmentation is a practical alternative to these methods. However, traditional interactive segmentation methods require a large number of user interactions, and recently proposed CNN-based interactive segmentation methods are limited by poor performance on previously unseen objects. To solve these problems, we propose a novel deep learning-based interactive segmentation method that not only has high efficiency due to only requiring clicks as user inputs but also generalizes well to a range of previously unseen objects. Specifically, we first encode user-provided interior margin points via our proposed exponentialized geodesic distance that enables a CNN to achieve a good initial segmentation result of both previously seen and unseen objects, then we use a novel information fusion method that combines the initial segmentation with only a few additional user clicks to efficiently obtain a refined segmentation. We validated our proposed framework through extensive experiments on 2D and 3D medical image segmentation tasks with a wide range of previously unseen objects that were not present in the training set. Experimental results showed that our proposed framework 1) achieves accurate results with fewer user interactions and less time compared with state-of-the-art interactive frameworks and 2) generalizes well to previously unseen objects.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.media.2021.102102

Cites

61 of 61 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 61 references

Research works printed but not linked: 1

  1. Vezhnevets, 2005, Growcut: interactive multi-label ND image segmentation by cellular automata, Graphicon, 1, 150

Cited by

Sorted by citations

Items 101–130 of 130

Related

See all

Metadata

Authors
Luo, X., Wang, G., Song, T., Zhang, J., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T., & Zhang, S.
Affiliations
  • University of Electronic Science and Technology of China (CN)
  • Sensetime (China) (CN)
  • Shanghai Jiao Tong University (CN)
  • Universitair Ziekenhuis Leuven (BE)
  • King's College London (GB)
Corresponding author
Wang, Guotai
ORCID
Venue
Medical Image Analysis
Publisher
Elsevier BV
Published
2021-08
Volume
72
Pages
102102
ISSN
1361-8415 (ISSN-L)
DOI
10.1016/j.media.2021.102102
PMC
PMC7613452
OpenAlex
W3159178846
Cited by
126
References
61
Citations by year
2026: 10 · 2025: 36 · 2024: 29 · 2023: 31 · 2022: 18 · 2021: 2
FWCI
7.19
Type
journal article
Language
en
Open access
Licence
Locations
  • Medical Image Analysis (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Lirias (submittedVersion, other-oa)
  • arXiv (Cornell University) (cc-by)
Funders
  • EPSRC
  • Wellcome Trust
  • National Natural Science Foundation of China
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Keywords
interactive segmentation, medical image segmentation, unseen objects, generalization, data fusion, organ segmentation, lesion segmentation, 3D medical images
MeSH terms
Deep Learning, Humans, Image Processing, Computer-Assisted, Neural Networks, Computer, Imaging, Three-Dimensional

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review