This review article provides an overview of deep learning-based segmentation approaches for quantitative brain MRI, highlighting their performance, speed, and properties. Deep learning, with its self-learning and generalization capabilities, is gaining popularity in medical image analysis due to its ability to handle large datasets and complex anatomical variations. The article discusses different deep learning architectures, such as patch-wise, semantic-wise, and cascaded CNNs, and their applications in segmenting normal brain structures and brain lesions. It also reviews the challenges and limitations of current deep learning methods, including the need for large datasets, data preprocessing, and overfitting prevention. The authors identify future directions, such as the development of more robust and generalizable deep learning models, the use of transfer learning, and the integration of unsupervised learning techniques. The review concludes by emphasizing the potential of deep learning in advancing the field of quantitative brain MRI analysis.This review article provides an overview of deep learning-based segmentation approaches for quantitative brain MRI, highlighting their performance, speed, and properties. Deep learning, with its self-learning and generalization capabilities, is gaining popularity in medical image analysis due to its ability to handle large datasets and complex anatomical variations. The article discusses different deep learning architectures, such as patch-wise, semantic-wise, and cascaded CNNs, and their applications in segmenting normal brain structures and brain lesions. It also reviews the challenges and limitations of current deep learning methods, including the need for large datasets, data preprocessing, and overfitting prevention. The authors identify future directions, such as the development of more robust and generalizable deep learning models, the use of transfer learning, and the integration of unsupervised learning techniques. The review concludes by emphasizing the potential of deep learning in advancing the field of quantitative brain MRI analysis.