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08764e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | """Datasets.
Stage1Dataset : random 3D patches (image -> 3-class semantic) for coarse segmentation.
Stage2Dataset : per-tooth ROI + sampled query points with GT dual-SDF, for the
implicit reconstruction core.
"""
import os
import numpy as np
from scipy import ndimage as ndi
from .geometry import sdf_from_mask, normals_from_sdf, trilinear_sample, \
sample_surface_and_random, canal_centerline
def _load(proc_dir, cid, cache):
if cid in cache:
return cache[cid]
d = np.load(os.path.join(proc_dir, f"{cid}.npz"))
obj = {k: d[k] for k in d.files}
cache[cid] = obj
return obj
# ----------------------------- STAGE 1 -----------------------------
class Stage1Dataset:
def __init__(self, proc_dir, cases, patch=(96, 96, 96),
samples_per_volume=4, train=True):
self.proc_dir = proc_dir
self.cases = cases
self.patch = np.array(patch)
self.spv = samples_per_volume
self.train = train
self.cache = {}
self.index = [(c, k) for c in cases for k in range(samples_per_volume)]
def __len__(self):
return len(self.index)
def __getitem__(self, i):
import torch
cid, _ = self.index[i]
d = _load(self.proc_dir, cid, self.cache)
img, sem = d["image"], d["sem"].astype(np.int64)
shape = np.array(img.shape)
ps = np.minimum(self.patch, shape)
# foreground-biased crop with 50% prob
if self.train and sem.any() and np.random.rand() < 0.7:
fg = np.argwhere(sem > 0)
c = fg[np.random.randint(len(fg))]
start = np.clip(c - ps // 2, 0, shape - ps)
else:
start = (np.random.rand(3) * (shape - ps + 1)).astype(int)
sl = tuple(slice(int(s), int(s + p)) for s, p in zip(start, ps))
ip = img[sl][None].astype(np.float32) # [1,X,Y,Z]
lp = sem[sl].astype(np.int64) # [X,Y,Z]
# pad if needed
if tuple(ip.shape[1:]) != tuple(self.patch):
pad = [(0, 0)] + [(0, int(self.patch[j] - ip.shape[1 + j])) for j in range(3)]
ip = np.pad(ip, pad)
lp = np.pad(lp, [(0, int(self.patch[j] - lp.shape[j])) for j in range(3)])
return dict(image=torch.from_numpy(ip), label=torch.from_numpy(lp))
# ----------------------------- STAGE 2 -----------------------------
class Stage2Dataset:
"""Enumerate (case, tooth-instance) ROIs. Each item yields the ROI image and a
fresh batch of sampled query points with GT tooth/canal SDF + normals + occupancy."""
def __init__(self, proc_dir, cases, cfg, train=True):
self.proc_dir = proc_dir
self.cfg = cfg
self.train = train
self.cache = {}
s2 = cfg["stage2"]
self.roi_mm = float(s2["roi_mm"])
self.roi_center = s2.get("roi_center", "com")
self.roi_vox = int(s2["roi_vox"])
self.npts = int(s2["points_per_tooth"])
self.near = float(s2["near_surface_ratio"])
self.sigma = float(s2["near_surface_sigma_mm"])
self.canal_frac = float(s2.get("canal_point_frac", 0.4))
self.center_frac = float(s2.get("centerline_frac", 0.0))
self.augment = bool(s2.get("augment", True))
# roi voxel spacing (isotropic) after resampling ROI to roi_vox
self.roi_sp = np.array([self.roi_mm / self.roi_vox] * 3, dtype=np.float32)
# build global instance list + a global id map (for the latent embedding)
self.items = []
for cid in cases:
d = _load(proc_dir, cid, self.cache)
ids = [int(v) for v in np.unique(d["inst"]) if v > 0]
for iid in ids:
self.items.append((cid, iid))
self.global_id = {k: n for n, k in enumerate(self.items)}
def num_instances(self):
return len(self.items)
def __len__(self):
return len(self.items)
def __getitem__(self, i):
import torch
from .roi import crop_roi
cid, iid = self.items[i]
d = _load(self.proc_dir, cid, self.cache)
crop = crop_roi(d, iid, self.roi_mm, self.roi_vox, center_mode=self.roi_center)
if crop is None:
return self.__getitem__((i + 1) % len(self))
img_r, solid_r, canal_r = crop["img"], crop["solid"], crop["canal"]
# 3D data augmentation (small-data regime: random axis flips + 90-deg rotations).
# Applied consistently to image + both masks so SDF/normals stay valid.
if self.train and self.augment:
for ax in range(3):
if np.random.rand() < 0.5:
img_r = np.flip(img_r, ax); solid_r = np.flip(solid_r, ax); canal_r = np.flip(canal_r, ax)
if np.random.rand() < 0.5:
k = np.random.randint(1, 4); pl = tuple(np.random.choice([0, 1, 2], 2, replace=False))
img_r = np.rot90(img_r, k, pl); solid_r = np.rot90(solid_r, k, pl); canal_r = np.rot90(canal_r, k, pl)
img_r = np.ascontiguousarray(img_r); solid_r = np.ascontiguousarray(solid_r); canal_r = np.ascontiguousarray(canal_r)
sdf_t = sdf_from_mask(solid_r, self.roi_sp)
sdf_c = sdf_from_mask(canal_r, self.roi_sp)
nrm_t = normals_from_sdf(sdf_t, self.roi_sp)
nrm_c = normals_from_sdf(sdf_c, self.roi_sp)
# sample query points: tooth surface + canal surface + canal CENTERLINE + random
n_canal = int(self.npts * self.canal_frac) if canal_r.any() else 0
n_center = int(self.npts * self.center_frac) if canal_r.any() else 0
n_tooth = self.npts - n_canal - n_center
pts_list = [sample_surface_and_random(solid_r, self.roi_sp, n_tooth,
self.near, self.sigma)]
if n_canal > 0:
pts_list.append(sample_surface_and_random(canal_r, self.roi_sp, n_canal,
near_ratio=0.9, sigma_mm=self.sigma))
if n_center > 0:
skel = canal_centerline(canal_r)
sk = np.argwhere(skel)
if len(sk) > 0:
idx = np.random.randint(0, len(sk), size=n_center)
cpts = sk[idx].astype(np.float32) + np.random.normal(0, 0.5, (n_center, 3))
pts_list.append(np.clip(cpts, 0, self.roi_vox - 1))
else:
pts_list.append(sample_surface_and_random(canal_r, self.roi_sp, n_center,
near_ratio=0.9, sigma_mm=self.sigma))
pts = np.concatenate(pts_list, axis=0)
gt_t = trilinear_sample(sdf_t, pts).astype(np.float32)
gt_c = trilinear_sample(sdf_c, pts).astype(np.float32)
gn_t = trilinear_sample(nrm_t, pts).astype(np.float32)
gn_c = trilinear_sample(nrm_c, pts).astype(np.float32)
# centerline membership flag (last n_center points are on/near the skeleton)
on_center = np.zeros(len(pts), np.float32)
if n_center > 0:
on_center[-n_center:] = 1.0
# convert voxel coords -> centered mm coords for the network
center_vox = (self.roi_vox - 1) / 2.0
coords_mm = (pts - center_vox) * self.roi_sp[None, :]
half_mm = self.roi_mm / 2.0
return dict(
image=torch.from_numpy(img_r[None].astype(np.float32)), # [1,V,V,V]
gid=torch.tensor(self.global_id[(cid, iid)], dtype=torch.long),
coords_mm=torch.from_numpy(coords_mm.astype(np.float32)), # [N,3]
half_mm=torch.tensor(half_mm, dtype=torch.float32),
sdf_tooth=torch.from_numpy(gt_t), # [N]
sdf_canal=torch.from_numpy(gt_c), # [N]
nrm_tooth=torch.from_numpy(gn_t), # [N,3]
nrm_canal=torch.from_numpy(gn_c), # [N,3]
on_center=torch.from_numpy(on_center), # [N]
case=cid, inst=iid,
)
def _fit(a, n):
"""Force a 3D array to shape (n,n,n) by crop/pad."""
out = np.zeros((n, n, n), dtype=a.dtype)
s = [min(n, a.shape[k]) for k in range(3)]
out[:s[0], :s[1], :s[2]] = a[:s[0], :s[1], :s[2]]
return out
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