AI for Surgical Planning

3D Organ Reconstruction

import numpy as np
from scipy.ndimage import gaussian_filter
from skimage import measure

class OrganReconstructor:
    def __init__(self, volume_data, spacing):
        self.volume = volume_data
        self.spacing = spacing

    def segment_organ(self, threshold_range):
        low, high = threshold_range
        mask = (self.volume >= low) & (self.volume <= high)
        return gaussian_filter(mask.astype(float), sigma=1.0) > 0.5

    def extract_surface(self, mask):
        verts, faces, normals, _ = measure.marching_cubes(mask, spacing=self.spacing)
        return verts, faces, normals

    def compute_volume(self, mask):
        return np.sum(mask) * np.prod(self.spacing)

Surgical Simulation

Tissue Deformation

Finite Element Method

class TissueSimulator:
    def __init__(self, mesh, material_props):
        self.E = material_props['youngs_modulus']
        self.nu = material_props['poisson_ratio']

    def simulate(self, forces, dt=0.001, steps=100):
        K = self._stiffness_matrix()
        u = np.zeros(K.shape[0])
        for _ in range(steps):
            u = np.linalg.solve(K, forces)
        return u.reshape(-1, 3)

Robotic Path Optimization

Minimum Energy Path

class SurgicalPathPlanner:
    def optimize_path(self, start, goal, n_waypoints=20):
        waypoints = np.linspace(start, goal, n_waypoints)
        for _ in range(1000):
            gradient = self._compute_gradient(waypoints)
            waypoints -= 0.01 * gradient
        return waypoints