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function computeMeasurements(mask, spacing_mm) const volume_mm3 = mask.sum() * spacing_mm[0] * spacing_mm[1] * spacing_mm[2]; const centroid = findCentroid(mask); const maxDiameter = computeMaxFeretDiameter(mask, spacing_mm); return volume_mm3, centroid, maxDiameter ;

// Render segmented muscle in red overlay const geometry = extractSurfaceMesh(mask); const material = new THREE.MeshPhongMaterial( color: 0xff3333, opacity: 0.6 ); const piriformMesh = new THREE.Mesh(geometry, material); scene.add(piriformMesh); 3. User Interface Mockup +--------------------------------------------------+ | [Piriform Feature] View: [Axial] [Sag] [3D] | | ┌─────────────┐ ┌─────────────────────┐ | | | Segmentation| | [3D view of pelvis]| | | [x] Muscle | | (piriform in red) | | | [ ] Aperture| | | | └─────────────┘ └─────────────────────┘ | | Measurements: | | Volume: 68.4 cm³ Max width: 4.2 cm | | Angle to midline: 32° | | [Export mask] [Measure distance] | +--------------------------------------------------+ 4. Training Data (for ML approach) | Dataset | Source | Label | |---------|--------|-------| | Pelvic CT 100 cases | TCIA (e.g., CT Pelvis) | Piriformis mask | | Hip MRI 50 cases | Private hospital data | Piriformis + aperture | | Augmentation | Rotation, scaling, elastic deform | — | piriform

// Inference const mask = model.predict(inputTensor); const centroid = findCentroid(mask)

// Post-process: largest connected component = piriformis const cleanedMask = connectedComponents(mask); const maxDiameter = computeMaxFeretDiameter(mask