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Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical\u2010level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state\u2010of\u2010the\u2010art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self\u2010supervised learning, generative models, few\u2010shot learning, and semi\u2010supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field\u2014including emerging algorithms, such as contrastive language\u2010image pretraining, and potential combinations across the methods discussed\u2014that can further increase the efficacy of image segmentation with limited labels.<\/jats:p>","DOI":"10.1002\/nbm.5143","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T02:10:30Z","timestamp":1711332630000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["A review of self\u2010supervised, generative, and few\u2010shot deep learning methods for data\u2010limited magnetic resonance imaging segmentation"],"prefix":"10.1002","volume":"37","author":[{"given":"Zelong","family":"Liu","sequence":"first","affiliation":[{"name":"BioMedical Engineering and Imaging Institute Icahn School of Medicine at Mount Sinai  New York New York 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