I am working on a project to identify tumors in MRI scans. I’m confused about whether I should be using a U-Net for semantic segmentation or something like Mask R-CNN. Does it matter if the model sees three separate tumors as one "class" or as individual objects?
3 answers
U-Net is very efficient. If you have limited GPU memory, U-Net is much easier to train on high-resolution 3D medical volumes compared to the much heavier Mask R-CNN.
In medical imaging, it usually depends on your clinical goal. Semantic segmentation (like U-Net) labels every pixel as "tumor" or "not tumor." If you just need to calculate the total tumor volume or area, semantic is enough. Instance segmentation (like Mask R-CNN) goes a step further and identifies Tumor A, Tumor B, and Tumor C as distinct entities. This is critical if you need to track the growth of individual lesions over time or count the number of nodules. For most MRI tasks, U-Net is the gold standard because its "skip connections" preserve the fine spatial details needed for accurate boundary detection, which is often more important than separating instances.
Is there a middle ground? Sometimes the boundaries between multiple tumors are so blurry that even a human expert can't tell where one ends and the other begins.
That’s exactly where "Panoptic Segmentation" comes in, Jason. It combines both semantic and instance logic. However, for the specific problem of blurry boundaries in MRI, we often use "Soft Dice Loss" or "Weighted Cross-Entropy" during training. These loss functions penalize the model more heavily for mistakes near the edges of the tumor. It doesn't necessarily separate the instances better, but it ensures that the "Semantic" mask is as anatomically accurate as possible. For overlapping tumors, we usually rely on a radiologist's manual "seed points" to help the model disambiguate.
I agree with Amy. Especially with 3D U-Net, you can process the entire volume at once, which is a huge advantage for identifying the true 3D shape of a lesion.