# High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey

arXiv: https://arxiv.org/abs/2011.08018 · HTML: https://mlsanity.com/paper/2011.08018/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 28 of 28)

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- [Learning with Explicit Shape Priors for Medical Image Segmentation](https://mlsanity.com/paper/2303.17967/) (2023)
- [Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts](https://mlsanity.com/paper/2108.11926/) (2021)
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- [SpineMamba: Enhancing 3D Spinal Segmentation in Clinical Imaging through Residual Visual Mamba Layers and Shape Priors](https://mlsanity.com/paper/2408.15887/) (2024)
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- [Effect of Prior-based Losses on Segmentation Performance: A Benchmark](https://mlsanity.com/paper/2201.02428/) (2022)
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- [CALF: A Conditionally Adaptive Loss Function to Mitigate Class-Imbalanced Segmentation](https://mlsanity.com/paper/2504.04458/) (2025)
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## Abstract (arXiv)

Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tasks. Despite early success, segmentation networks may still generate anatomically aberrant segmentations, with holes or inaccuracies near the object boundaries. To mitigate this effect, recent research works have focused on incorporating spatial information or prior knowledge to enforce anatomically plausible segmentation. If the integration of prior knowledge in image segmentation is not a new topic in classical optimization approaches, it is today an increasing trend in CNN based image segmentation, as shown by the growing literature on the topic. In this survey, we focus on high level prior, embedded at the loss function level. We categorize the articles according to the nature of the prior: the object shape, size, topology, and the inter-regions constraints. We highlight strengths and limitations of current approaches, discuss the challenge related to the design and the integration of prior-based losses, and the optimization strategies, and draw future research directions.