Editing Medical Image Segmentation (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Segmentation mask''' — A pixel-wise (or voxel-wise in 3D) map labeling each image element with its class.
* '''Semantic segmentation''' — Labeling every pixel with a class (e.g., liver, tumor, background); no instance distinction.
* '''Instance segmentation''' — Distinguishing individual instances of the same class (e.g., each separate cell nucleus).
* '''Panoptic segmentation''' — Combines semantic and instance segmentation; labels all pixels with class and instance ID.
* '''U-Net''' — A encoder-decoder architecture with skip connections; the dominant framework for medical image segmentation.
* '''Skip connections''' — Direct connections from encoder to decoder that preserve high-resolution spatial features.
* '''V-Net''' — 3D extension of U-Net for volumetric medical image segmentation.
* '''nnU-Net''' — A self-configuring U-Net framework that automatically adapts to any medical imaging dataset; widely used baseline.
* '''Intersection over Union (IoU)''' — Primary segmentation metric: area of overlap / area of union between predicted and ground truth masks.
* '''Dice coefficient''' — 2 × |A ∩ B| / (|A| + |B|); equivalent to F1 score for segmentation; used in Dice loss.
* '''Dice loss''' — 1 - Dice coefficient; directly optimizes the Dice metric; better than cross-entropy for imbalanced segmentation.
* '''CT (Computed Tomography)''' — 3D medical imaging using X-rays; voxel-based volumetric data.
* '''MRI (Magnetic Resonance Imaging)''' — 3D imaging using magnetic fields; soft tissue contrast superior to CT.
* '''Histopathology''' — Microscopic study of tissue; whole-slide images (WSI) can be gigapixel-scale.
* '''SAM (Segment Anything Model)''' — Meta's foundation model for promptable segmentation; adapted to medical imaging (MedSAM, SAM-Med).
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