# Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation

arXiv: https://arxiv.org/abs/1806.08437 · HTML: https://mlsanity.com/paper/1806.08437/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2018.

## Built on by (most cited first, 25 of 25)

- [SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2111.13495/) (2021)
- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images](https://mlsanity.com/paper/2403.08689/) (2024)
- [ConvFormer: Combining CNN and Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2211.08564/) (2022)
- [MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/2112.01767/) (2021)
- [MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation](https://mlsanity.com/paper/2409.03062/) (2024)
- [Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data](https://mlsanity.com/paper/2501.04735/) (2025)
- [Shape-intensity knowledge distillation for robust medical image segmentation](https://mlsanity.com/paper/2409.17503/) (2024)
- [Weakly Supervised Volumetric Image Segmentation with Deformed Templates](https://mlsanity.com/paper/2106.03987/) (2021)
- [Supervision by Denoising for Medical Image Segmentation](https://mlsanity.com/paper/2202.02952/) (2022)
- [Opinions Vary? Diagnosis First!](https://mlsanity.com/paper/2202.06505/) (2022)
- [Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle](https://mlsanity.com/paper/2208.03016/) (2022)
- [Lesion Elevation Prediction from Skin Images Improves Diagnosis](https://mlsanity.com/paper/2408.02792/) (2024)
- [SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation](https://mlsanity.com/paper/2409.08652/) (2024)
- [SPAC-Net: Rethinking Point Cloud Completion with Structural Prior](https://mlsanity.com/paper/2411.15066/) (2024)
- [GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis](https://mlsanity.com/paper/2502.16748/) (2025)
- [Muscle volume quantification: guiding transformers with anatomical priors](https://mlsanity.com/paper/2310.20355/) (2023)
- [Effect of Prior-based Losses on Segmentation Performance: A Benchmark](https://mlsanity.com/paper/2201.02428/) (2022)
- [Contrastive and Selective Hidden Embeddings for Medical Image Segmentation](https://mlsanity.com/paper/2201.08779/) (2022)
- [Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series](https://mlsanity.com/paper/2312.05148/) (2023)
- [Contour Field based Elliptical Shape Prior for the Segment Anything Model](https://mlsanity.com/paper/2504.12556/) (2025)
- [GazeTrack: High-Precision Eye Tracking Based on Regularization and Spatial Computing](https://mlsanity.com/paper/2511.22607/) (2025)
- [Real-time topology-aware M-mode OCT segmentation for robotic deep anterior lamellar keratoplasty (DALK) guidance](https://mlsanity.com/paper/2602.02798/) (2026)

## Abstract (arXiv)

Semantic segmentation is an important preliminary step towards automatic medical image interpretation. Recently deep convolutional neural networks have become the first choice for the task of pixel-wise class prediction. While incorporating prior knowledge about the structure of target objects has proven effective in traditional energy-based segmentation approaches, there has not been a clear way for encoding prior knowledge into deep learning frameworks. In this work, we propose a new loss term that encodes the star shape prior into the loss function of an end-to-end trainable fully convolutional network (FCN) framework. We penalize non-star shape segments in FCN prediction maps to guarantee a global structure in segmentation results. Our experiments demonstrate the advantage of regularizing FCN parameters by the star shape prior and our results on the ISBI 2017 skin segmentation challenge data set achieve the first rank in the segmentation task among 21 participating teams.