Image segmentation is the task of labeling an image at the pixel level, and it underpins much of modern medical imaging, autonomous perception, and scene understanding. Here we break down segmentation architectures, loss functions, and evaluation practices, with frequent attention to the medical settings where boundary accuracy and robustness matter most. Each piece traces the method back to its source research.
A model trained to segment the prostate in MRI scans from one institution routinely fails when presented with scans from another hospital that uses a different scanner, a different field strength, or a different slice thickness. This is not a…
Give the Segment Anything Model a box drawn around a bone in an X-ray and it does something close to magic. Give it the same kind of box on an ultrasound image of a wrist and it frequently falls apart,…
Sam2Rad Explained: Teaching SAM2 to Prompt Itself on Ultrasound Read More »
Medical image segmentation sounds like a narrow technical task, draw a boundary around the diseased tissue, but the difficulty hides in what that boundary actually needs to capture. A tumor in a breast ultrasound image does not have a clean…
Why 3DL-Net’s Dendritic Neurons Only Help When Paired With Its Pyramid Module Read More »
Segmenting a medical image sounds simple until you try it. The boundary between a tumor and healthy tissue is often blurry rather than crisp, because soft tissue does not offer the kind of contrast a natural photograph does. The same…
Attention Mechanisms Reshaping Medical Image Segmentation Read More »
Fully retraining a billion parameter foundation model every time it stumbles on a new domain is expensive, slow, and risks quietly erasing the very generality that made the model useful in the first place. A team spanning the National University…
Med-SA Explained, Adapting SAM to Medicine With 2 Percent of Its Weights Read More »