# 3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection

arXiv: https://arxiv.org/abs/2402.12128 · HTML: https://mlsanity.com/paper/2402.12128/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [Self-Supervised Learning of 3D Human Pose using Multi-view Geometry](https://mlsanity.com/paper/1903.02330/) (2019)
- [CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision](https://mlsanity.com/paper/2203.01475/) (2022)
- [DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes](https://mlsanity.com/paper/1803.09340/) (2018)
- [Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images](https://mlsanity.com/paper/2007.05448/) (2020)
- [Deep Distance Transform for Tubular Structure Segmentation in CT Scans](https://mlsanity.com/paper/1912.03383/) (2019)
- [Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation](https://mlsanity.com/paper/2107.00583/) (2021)
- [Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations](https://mlsanity.com/paper/2205.06779/) (2022)
- [Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images](https://mlsanity.com/paper/2206.06665/) (2022)
- [Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection](https://mlsanity.com/paper/1904.05956/) (2019)
- [Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity](https://mlsanity.com/paper/2302.02125/) (2023)
- [Tracing in 2D to Reduce the Annotation Effort for 3D Deep Delineation](https://mlsanity.com/paper/1811.10508/) (2018)
- [3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations](https://mlsanity.com/paper/2302.03299/) (2023)
- [3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images](https://mlsanity.com/paper/2309.08481/) (2023)
- [Weakly Supervised Volumetric Image Segmentation with Deformed Templates](https://mlsanity.com/paper/2106.03987/) (2021)
- [Enforcing connectivity of 3D linear structures using their 2D projections](https://mlsanity.com/paper/2207.06832/) (2022)

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

- [Top-K Maximum Intensity Projection Priors for 3D Liver Vessel Segmentation](https://mlsanity.com/paper/2503.03367/) (2025)
- [Uncertainty-Aware Information Pursuit for Interpretable and Reliable Medical Image Analysis](https://mlsanity.com/paper/2506.16742/) (2025)
- [Multi-Temporal Frames Projection for Dynamic Processes Fusion in Fluorescence Microscopy](https://mlsanity.com/paper/2601.10392/) (2026)

## Abstract (arXiv)

Vascular structure segmentation plays a crucial role in medical analysis and clinical applications. The practical adoption of fully supervised segmentation models is impeded by the intricacy and time-consuming nature of annotating vessels in the 3D space. This has spurred the exploration of weakly-supervised approaches that reduce reliance on expensive segmentation annotations. Despite this, existing weakly supervised methods employed in organ segmentation, which encompass points, bounding boxes, or graffiti, have exhibited suboptimal performance when handling sparse vascular structure. To alleviate this issue, we employ maximum intensity projection (MIP) to decrease the dimensionality of 3D volume to 2D image for efficient annotation, and the 2D labels are utilized to provide guidance and oversight for training 3D vessel segmentation model. Initially, we generate pseudo-labels for 3D blood vessels using the annotations of 2D projections. Subsequently, taking into account the acquisition method of the 2D labels, we introduce a weakly-supervised network that fuses 2D-3D deep features via MIP to further improve segmentation performance. Furthermore, we integrate confidence learning and uncertainty estimation to refine the generated pseudo-labels, followed by fine-tuning the segmentation network. Our method is validated on five datasets (including cerebral vessel, aorta and coronary artery), demonstrating highly competitive performance in segmenting vessels and the potential to significantly reduce the time and effort required for vessel annotation. Our code is available at: https://github.com/gzq17/Weakly-Supervised-by-MIP.