# Shape-Aware Organ Segmentation by Predicting Signed Distance Maps

arXiv: https://arxiv.org/abs/1912.03849 · HTML: https://mlsanity.com/paper/1912.03849/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 30 of 41)

- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2105.07059/) (2021)
- [Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective](https://mlsanity.com/paper/2302.01735/) (2023)
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- [FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms](https://mlsanity.com/paper/2405.18087/) (2024)
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- [BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding](https://mlsanity.com/paper/2202.00002/) (2022)
- [FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation](https://mlsanity.com/paper/2109.02920/) (2021)
- [JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes](https://mlsanity.com/paper/2207.07895/) (2022)
- [Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2303.05745/) (2023)
- [Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functions](https://mlsanity.com/paper/2303.05966/) (2023)
- [Contour-Aware Equipotential Learning for Semantic Segmentation](https://mlsanity.com/paper/2210.00223/) (2022)
- [Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation](https://mlsanity.com/paper/2203.05682/) (2022)
- [SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform Regression](https://mlsanity.com/paper/2405.16813/) (2024)
- [Shape-intensity knowledge distillation for robust medical image segmentation](https://mlsanity.com/paper/2409.17503/) (2024)
- [Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation](https://mlsanity.com/paper/2509.13834/) (2025)
- [CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy](https://mlsanity.com/paper/2101.04266/) (2021)
- [Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation](https://mlsanity.com/paper/2106.01596/) (2021)
- [Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation](https://mlsanity.com/paper/2202.02371/) (2022)
- [Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI](https://mlsanity.com/paper/2203.01089/) (2022)
- [Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2209.08355/) (2022)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans](https://mlsanity.com/paper/2303.05126/) (2023)
- [Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images](https://mlsanity.com/paper/2410.12831/) (2024)
- [A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT Scans](https://mlsanity.com/paper/2505.01644/) (2025)
- [Pushing the Envelope of Thin Crack Detection](https://mlsanity.com/paper/2101.03326/) (2021)
- [HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation](https://mlsanity.com/paper/2109.05742/) (2021)
- [FourierLoss: Shape-Aware Loss Function with Fourier Descriptors](https://mlsanity.com/paper/2309.12106/) (2023)
- [Uncertainty-aware retinal layer segmentation in OCT through probabilistic signed distance functions](https://mlsanity.com/paper/2412.04935/) (2024)

## Abstract (arXiv)

In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness. Since there is a rigorous mapping between the Signed Distance Map (SDM) calculated from object boundary contours and the binary segmentation map, we exploit the feasibility of learning the SDM directly from medical scans. By converting the segmentation task into predicting an SDM, we show that our proposed method retains superior segmentation performance and has better smoothness and continuity in shape. To leverage the complementary information in traditional segmentation training, we introduce an approximated Heaviside function to train the model by predicting SDMs and segmentation maps simultaneously. We validate our proposed models by conducting extensive experiments on a hippocampus segmentation dataset and the public MICCAI 2015 Head and Neck Auto Segmentation Challenge dataset with multiple organs. While our carefully designed backbone 3D segmentation network improves the Dice coefficient by more than 5% compared to current state-of-the-arts, the proposed model with SDM learning produces smoother segmentation results with smaller Hausdorff distance and average surface distance, thus proving the effectiveness of our method.