What does BMAS mean in UNCLASSIFIED

Working Principle

The BioClinica Multi Atlas Segmentation (BMAS) technique consists of using multiple atlas images that are acquired from the same imaging modality to guide the segmentation task on a target image. The concept is based upon the idea that several atlases can be compared with each other, and then combined using various algorithms like majority voting, masking or merging techniques. After this process, a refined result can be obtained which allows to accurately delineate anatomical regions within the target image. This method can also be applied in conjunction with machine-learning techniques such as artificial neural networks (ANN) or support vector machines (SVM). These techniques allow for faster computation times when compared with traditional methods like manual contouring due to their ability to learn from given examples quickly and accurately. Additionally, these techniques are less prone to errors caused by human subjectivity when making decisions related to segmentation tasks.

Advantages

One main advantage of using BMAS over traditional methods is its accuracy rate; this method has been shown to produce more accurate results than manual contouring and other existing automated methods like snakes or active shape models. Additionally, it requires fewer resources because it only needs access to multiple atlas images instead of large datasets of labeled data that many machine learning methods require. Lastly, this technique is also faster than some manual approaches since it does not need manual tracing of objects in each image. Therefore, BMAS presents an attractive option for dealing with medical imaging tasks efficiently while maintaining accuracy levels comparable with traditional methods.