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.
The retina is one of the only places in the human body where blood vessels can be photographed directly without surgery, which is exactly why so much diagnostic attention focuses on it. Vessels enter and exit through the optic nerve…
How MaskVSC Reconnects Broken Retinal Blood Vessels Read More »
Every echocardiogram report that lists an ejection fraction, a chamber volume, or a wall thickness represents a human being drawing a line around a moving, low contrast, occasionally noisy structure on ultrasound. Clinical guidelines from the American Society of Echocardiography…
Self Supervised Learning: How AI Learned to Read Heart Ultrasounds Without Human Labels Read More »
Deep learning segmentation models are, by now, quite good at their job when they have enough data to learn from. The actual constraint most teams run into is upstream of the model entirely, getting enough pixel level annotated examples to…
GenSeg And Training Medical AI With Barely Any Data Read More »
Segmenting the internal structures in a CT or MRI scan, tracing the exact outline of a heart chamber, an organ, or a tumor, is essential groundwork for a long list of clinical applications, and most of the strong supervised segmentation…
How Bidirectional Copy-Paste Closes the Labeled Unlabeled Gap Read More »
Medical image segmentation has a labeling problem that computer vision in general does not face nearly as badly. Drawing a box around a cat in a photo takes seconds. Tracing the exact boundary of a pancreas across dozens of CT…
SDCL: Two Students Learning From Each Other’s Mistakes Fix a Blind Spot in Segmentation Read More »
Contrast enhanced imaging is genuinely useful, which is exactly why it became standard practice. In MRI, gadolinium based agents make the difference in signal between tumor tissue and normal tissue much easier to see, and in CT, iodinated contrast media…
A Contrast Free Tumor AI Owes Little to Its Text Model Read More »
Cross modal knowledge distillation for RGBD data almost always follows the same recipe. Train a multimodal teacher that consumes both RGB and Depth, then train one or more single modality student models to mimic what that teacher learned, so the…
CroDiNo-KD: RGB and Depth Models That Train Each Other, No Teacher Needed Read More »
Ask anyone who has tried to build a medical image segmentation model what the hardest part is, and the honest answer is rarely the neural network architecture. It is getting enough labeled data. A useful segmentation dataset needs pixel accurate…
DFCPS: Teaching a Polyp Detector With Barely Any Labels Read More »
Imagine training a heart scan segmentation model with exactly one labeled patient. That is not a hypothetical stress test, it is the actual one percent labeled setting a team from Zhejiang University and Ritsumeikan University put their method through, and…
SemSim Adds Semantic Similarity to FixMatch Segmentation Read More »
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…