mlsanityData to 7 Oct 2026

Weakly Supervised Volumetric Image Segmentation with Deformed Templates

arXiv 2106.03987 ↗First posted 7 Jun 2021cs.CVcited by 3

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Abstract (arXiv)

There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision of a subset of 2D slices of the 3D volume. We propose an approach to volume segmentation that is truly weakly-supervised in the sense that we only need to provide a sparse set of 3D points on the surface of target objects instead of detailed 2D masks. We use the 3D points to deform a 3D template so that it roughly matches the target object outlines and we introduce an architecture that exploits the supervision it provides to train a network to find accurate boundaries. We evaluate our approach on Computed Tomography (CT), Magnetic Resonance Imagery (MRI) and Electron Microscopy (EM) image datasets and show that it substantially reduces the required amount of effort.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 35 of its 55 references that are in the map. First posted on arXiv 7 Jun 2021.

Builds on

35 of 55 references.

References · 35

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23 more
  1. 2019Fast Interactive Object Annotation with Curve-GCNcited by 95
  2. 2018Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learningcited by 81
  3. 2019Unsupervised Object Segmentation by Redrawingcited by 74
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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 31
  2. 2023Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit Fieldscited by 6
  3. 20243D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projectioncited by 3