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The medical imaging literature has witnessed remarkable progress inhigh-performing segmentation models based on convolutional neural networks.Despite the new performance highs, the recent advanced segmentation modelsstill require large, representative, and high quality annotated datasets.However, rarely do we have a perfect training dataset, particularly in thefield of medical imaging, where data and annotations are both expensive toacquire. Recently, a large body of research has studied the problem of medicalimage segmentation with imperfect datasets, tackling two major datasetlimitations: scarce annotations where only limited annotated data is availablefor training, and weak annotations where the training data has only sparseannotations, noisy annotations, or image-level annotations. In this article, weprovide a detailed review of the solutions above, summarizing both thetechnical novelties and empirical results. We further compare the benefits andrequirements of the surveyed methodologies and provide our recommendedsolutions. We hope this survey article increases the community awareness of thetechniques that are available to handle imperfect medical image segmentationdatasets.

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation