LEARNING TO SEGMENT FROM NOISY ANNOTATIONS: A SPATIAL CORRECTION APPROACH

Abstract

Noisy labels can significantly affect the performance of deep neural networks (DNNs). In medical image segmentation tasks, annotations are error-prone due to the high demand in annotation time and in the annotators' expertise. Existing methods mostly assume noisy labels in different pixels are i.i.d. However, segmentation label noise usually has strong spatial correlation and has prominent bias in distribution. In this paper, we propose a novel Markov model for segmentation noisy annotations that encodes both spatial correlation and bias. Further, to mitigate such label noise, we propose a label correction method to recover true label progressively. We provide theoretical guarantees of the correctness of the proposed method. Experiments show that our approach outperforms current stateof-the-art methods on both synthetic and real-world noisy annotations. 1

1. INTRODUCTION

Noisy annotations are inevitable in large scale datasets, and can heavily impair the performance of deep neural networks (DNNs) due to their strong memorization power (Zhang et al., 2016; Arpit et al., 2017) . Image segmentation also suffers from the label noise problem. For medical images, segmentation quality is highly dependent on human annotators' expertise and time spent. In practice, medical students and residents in training are often recruited to annotate, potentially introducing errors (Gurari et al., 2015; Kohli et al., 2017) . We also note even among experts, there can be poor consensus in terms of objects' location and boundary (Menze et al., 2014; Joskowicz et al., 2018; Zhang et al., 2020a) . Furthermore, segmentation annotations require pixel/voxel-level detailed delineations of the objects of interest. Annotating objects involving complex boundaries and structures are especially time-consuming. Thus, errors can naturally be introduced when annotating at scale. Segmentation is the first step of most analysis pipelines. Inaccurate segmentation can introduce error into measurements such as the morphology, which can be important for downstream diagnosis and prognostic tasks (Wang et al., 2019a; Nafe et al., 2005) . Therefore, it is important to develop robust training methods against segmentation label noise. However, despite many existing methods addressing label noise in classification tasks (Patrini et al., 2017; Yu et al., 2019; Zhang & Sabuncu, 2018; Li et al., 2020; Liu et al., 2020; Zhang et al., 2021; Xia et al., 2021) , limited progress has been made in the context of image segmentation. A few existing segmentation label noise approaches (Zhu et al., 2019; Zhang et al., 2020b; a) directly apply methods in classification label noise. However, these methods assume the label noise for each pixel is i.i.d. (independent and identically distributed). This assumption is not realistic in the segmentation context, where annotation is often done by brushes, and error is usually introduced near the boundary of objects. Regions further away from the boundary are less likely to be mislabeled (see Fig. 1c for an illustration). Therefore, in segmentation tasks, label noise of pixels has to be spatially correlated. An i.i.d. label noise will result in unrealistic annotations as in Fig. 1b . We propose a novel label noise model for segmentation annotations. Our model simulates the real annotation scenario, where an annotator uses a brush to delineate the boundary of an object. The noisy boundary can be considered a random yet continuous distortion of the true boundary. To



Codes are available at https://github.com/michaelofsbu/SpatialCorrection. 1

