# Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks Source: [Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:24:52.728Z Authors: Arjun D. Desai, Garry E. Gold, Brian A. Hargreaves, Akshay S. Chaudhari Published: 2019-02-05T23:39:17.000Z ## Abstract High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications. While manual segmentation is accepted as the gold-standard, recent advances in deep learning and convolutional neural networks (CNNs) have shown promise for efficient automatic segmentation of soft tissues. However, due to the stochastic nature of deep learning and the multitude of hyperparameters in training networks, predicting network behavior is challenging. In this paper, we quantify the impact of three factors associated with CNN segmentation performance: network architecture, training loss functions, and training data characteristics. We evaluate the impact of these variations on the segmentation of femoral cartilage and propose potential modifications to CNN architectures and training protocols to train these models with confidence. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()