# Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation

arXiv: https://arxiv.org/abs/1902.04099 · HTML: https://mlsanity.com/paper/1902.04099/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2019.

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

- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [Mutual Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.09960/) (2021)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [Progressively Normalized Self-Attention Network for Video Polyp Segmentation](https://mlsanity.com/paper/2105.08468/) (2021)
- [SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More](https://mlsanity.com/paper/2408.04579/) (2024)
- [Camouflaged Object Detection via Context-aware Cross-level Fusion](https://mlsanity.com/paper/2207.13362/) (2022)
- [Duplex Contextual Relation Network for Polyp Segmentation](https://mlsanity.com/paper/2103.06725/) (2021)
- [BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations](https://mlsanity.com/paper/2212.03498/) (2022)
- [BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2110.14775/) (2021)
- [FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images](https://mlsanity.com/paper/2210.03189/) (2022)
- [SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation](https://mlsanity.com/paper/2511.19425/) (2025)
- [Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks](https://mlsanity.com/paper/2210.04285/) (2022)
- [Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey](https://mlsanity.com/paper/2401.11734/) (2024)
- [Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors](https://mlsanity.com/paper/2207.13502/) (2022)
- [Edge-aware Feature Aggregation Network for Polyp Segmentation](https://mlsanity.com/paper/2309.10523/) (2023)
- [Multi-scale Information Sharing and Selection Network with Boundary Attention for Polyp Segmentation](https://mlsanity.com/paper/2405.11151/) (2024)
- [PSTNet: Enhanced Polyp Segmentation with Multi-scale Alignment and Frequency Domain Integration](https://mlsanity.com/paper/2409.08501/) (2024)
- [Contour Flow Constraint: Preserving Global Shape Similarity for Deep Learning based Image Segmentation](https://mlsanity.com/paper/2504.09384/) (2025)
- [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)
- [SSTFB: Leveraging self-supervised pretext learning and temporal self-attention with feature branching for real-time video polyp segmentation](https://mlsanity.com/paper/2406.10200/) (2024)
- [Approximation of dilation-based spatial relations to add structural constraints in neural networks](https://mlsanity.com/paper/2102.10923/) (2021)
- [High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss](https://mlsanity.com/paper/2211.02419/) (2022)
- [FLDNet: A Foreground-Aware Network for Polyp Segmentation Leveraging Long-Distance Dependencies](https://mlsanity.com/paper/2309.05987/) (2023)
- [PI-Att: Topology Attention for Segmentation Networks through Adaptive Persistence Image Representation](https://mlsanity.com/paper/2408.08038/) (2024)
- [BoundarySeg:Boundary-Aware Multi-Task Learning for Semi-Supervised 3D Medical Image Segmentation with Limited Unlabeled Data](https://mlsanity.com/paper/2505.09829/) (2025)
- [CL-Polyp: A Contrastive Learning-Enhanced Network for Accurate Polyp Segmentation](https://mlsanity.com/paper/2507.07154/) (2025)
- [Unlocking Generalization in Polyp Segmentation with DINO Self-Attention "keys"](https://mlsanity.com/paper/2512.13376/) (2025)

## Abstract (arXiv)

Image segmentation is a primary task in many medical applications. Recently, many deep networks derived from U-Net have been extensively used in various medical image segmentation tasks. However, in most of the cases, networks similar to U-net produce coarse and non-smooth segmentations with lots of discontinuities. To improve and refine the performance of U-Net like networks, we propose the use of parallel decoders which along with performing the mask predictions also perform contour prediction and distance map estimation. The contour and distance map aid in ensuring smoothness in the segmentation predictions. To facilitate joint training of three tasks, we propose a novel architecture called Psi-Net with a single encoder and three parallel decoders (thus having a shape of Psi), one decoder to learns the segmentation mask prediction and other two decoders to learn the auxiliary tasks of contour detection and distance map estimation. The learning of these auxiliary tasks helps in capturing the shape and the boundary information. We also propose a new joint loss function for the proposed architecture. The loss function consists of a weighted combination of Negative Log likelihood and Mean Square Error loss. We have used two publicly available datasets: 1) Origa dataset for the task of optic cup and disc segmentation and 2) Endovis segment dataset for the task of polyp segmentation to evaluate our model. We have conducted extensive experiments using our network to show our model gives better results in terms of segmentation, boundary and shape metrics.