# Weakly Supervised Volumetric Image Segmentation with Deformed Templates

arXiv: https://arxiv.org/abs/2106.03987 · HTML: https://mlsanity.com/paper/2106.03987/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 55 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 30 of 35)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [Simple Does It: Weakly Supervised Instance and Semantic Segmentation](https://mlsanity.com/paper/1603.07485/) (2016)
- [Data augmentation using learned transformations for one-shot medical image segmentation](https://mlsanity.com/paper/1902.09383/) (2019)
- [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://mlsanity.com/paper/1711.09081/) (2017)
- [Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++](https://mlsanity.com/paper/1803.09693/) (2018)
- [Deep Snake for Real-Time Instance Segmentation](https://mlsanity.com/paper/2001.01629/) (2020)
- [Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1904.11693/) (2019)
- [Fast Interactive Object Annotation with Curve-GCN](https://mlsanity.com/paper/1903.06874/) (2019)
- [Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning](https://mlsanity.com/paper/1803.01207/) (2018)
- [Unsupervised Object Segmentation by Redrawing](https://mlsanity.com/paper/1905.13539/) (2019)
- [Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up](https://mlsanity.com/paper/1903.02827/) (2019)
- [W-Net: A Deep Model for Fully Unsupervised Image Segmentation](https://mlsanity.com/paper/1711.08506/) (2017)
- [Emergence of Object Segmentation in Perturbed Generative Models](https://mlsanity.com/paper/1905.12663/) (2019)
- [Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/1907.12303/) (2019)
- [Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images](https://mlsanity.com/paper/2007.05448/) (2020)
- [Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data](https://mlsanity.com/paper/1912.03681/) (2019)
- [Semi-supervised few-shot learning for medical image segmentation](https://mlsanity.com/paper/2003.08462/) (2020)
- [Scribble-based Domain Adaptation via Co-segmentation](https://mlsanity.com/paper/2007.03632/) (2020)
- [Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning](https://mlsanity.com/paper/1802.09129/) (2018)
- [Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations](https://mlsanity.com/paper/1904.07934/) (2019)
- [Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks](https://mlsanity.com/paper/1806.05104/) (2018)
- [Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation](https://mlsanity.com/paper/1806.08437/) (2018)
- [Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules](https://mlsanity.com/paper/1707.01086/) (2017)
- [Unsupervised Deep Learning for Bayesian Brain MRI Segmentation](https://mlsanity.com/paper/1904.11319/) (2019)
- [Weakly supervised segmentation from extreme points](https://mlsanity.com/paper/1910.01236/) (2019)

## Built on by (most cited first, 3 of 3)

- [MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision](https://mlsanity.com/paper/2308.16139/) (2023)
- [Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit Fields](https://mlsanity.com/paper/2309.17329/) (2023)
- [3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection](https://mlsanity.com/paper/2402.12128/) (2024)

## 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.