# Imperfect Segmentation Labels: How Much Do They Matter?

Source: [Imperfect Segmentation Labels: How Much Do They Matter?](<https://www.sota2.com/research/paper/68fa64da1e209131cea06b91>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:12:58.577Z

Authors: Nicholas Heller, Joshua Dean, Nikolaos Papanikolopoulos

Published: 2018-06-12T15:54:42.000Z

## Abstract

Labeled datasets for semantic segmentation are imperfect, especially in medical imaging where borders are often subtle or ill-defined. Little work has been done to analyze the effect that label errors have on the performance of segmentation methodologies. Here we present a large-scale study of model performance in the presence of varying types and degrees of error in training data. We trained U-Net, SegNet, and FCN32 several times for liver segmentation with 10 different modes of ground-truth perturbation. Our results show that for each architecture, performance steadily declines with boundary-localized errors, however, U-Net was significantly more robust to jagged boundary errors than the other architectures. We also found that each architecture was very robust to non-boundary-localized errors, suggesting that boundary-localized errors are fundamentally different and more challenging problem than random label errors in a classification setting.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1806.04618>)

[PDF](<https://arxiv.org/pdf/1806.04618.pdf>)