Medical image analysis

• Medical image segmentation typically faces limited datasets. • Dataset limitations are broadly grouped into scarce and weak annotations. • Scarce annotations can be addressed proactively via cost-effective annotation or by leveraging external labeled or unlabeled datasets. • Scarce annotations can be addressed reactively via various forms of conditional random fields as post processing. • Weak annotations may manifest as sparse, noisy, or only image-level labels. • Sparse and noisy annotations can leveraged via selective and noise-resilient loss functions, respectively. • Image-level labels can leveraged via various forms of class activation maps. • Recommended solutions based on a cost-gain trade-off are provided. The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire. Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. We further compare the benefits and requirements of the surveyed methodologies and provide our recommended solutions. We hope this survey article increases the community awareness of the techniques that are available to handle imperfect medical image segmentation datasets.

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation