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Add transform to apply orientation to guidance #1537

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2 changes: 2 additions & 0 deletions sample-apps/radiology/lib/infers/deepedit.py
Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@

from typing import Callable, Sequence, Union

from lib.transforms.transforms import OrientationGuidanceMultipleLabelDeepEditd
from monai.apps.deepedit.transforms import (
AddGuidanceFromPointsDeepEditd,
AddGuidanceSignalDeepEditd,
Expand Down Expand Up @@ -87,6 +88,7 @@ def pre_transforms(self, data=None):
if self.type == InferType.DEEPEDIT:
t.extend(
[
OrientationGuidanceMultipleLabelDeepEditd(ref_image="image", label_names=self.labels),
AddGuidanceFromPointsDeepEditd(ref_image="image", guidance="guidance", label_names=self.labels),
Resized(keys="image", spatial_size=self.spatial_size, mode="area"),
ResizeGuidanceMultipleLabelDeepEditd(guidance="guidance", ref_image="image"),
Expand Down
34 changes: 34 additions & 0 deletions sample-apps/radiology/lib/transforms/transforms.py
Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@

import numpy as np
import torch
from einops import rearrange
from monai.config import KeysCollection, NdarrayOrTensor
from monai.transforms import CropForeground, GaussianSmooth, Randomizable, Resize, ScaleIntensity, SpatialCrop
from monai.transforms.transform import MapTransform, Transform
Expand Down Expand Up @@ -505,3 +506,36 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, N
if d.get(cache_key) is None:
d[cache_key] = copy.deepcopy(d[key])
return d


class OrientationGuidanceMultipleLabelDeepEditd(Transform):
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def __init__(self, ref_image: str, label_names=None):
"""
Convert the guidance to the RAS orientation
"""
self.ref_image = ref_image
self.label_names = label_names

def transform_points(self, point, affine):
"""transform point to the coordinates of the transformed image
point: numpy array [bs, N, 3]
"""
bs, N = point.shape[:2]
point = np.concatenate((point, np.ones((bs, N, 1))), axis=-1)
point = rearrange(point, "b n d -> d (b n)")
point = affine @ point
point = rearrange(point, "d (b n)-> b n d", b=bs)[:, :, :3]
return point

def __call__(self, data):
d: Dict = dict(data)
for key_label in self.label_names.keys():
points = d.get(key_label, [])
if len(points) < 1:
continue
reoriented_points = self.transform_points(
np.array(points)[None],
np.linalg.inv(d[self.ref_image].meta["affine"].numpy()) @ d[self.ref_image].meta["original_affine"],
)
d[key_label] = reoriented_points[0]
return d
6 changes: 4 additions & 2 deletions sample-apps/radiology/main.py
Original file line number Diff line number Diff line change
Expand Up @@ -287,7 +287,7 @@ def main():

parser = argparse.ArgumentParser()
parser.add_argument("-s", "--studies", default=studies)
parser.add_argument("-m", "--model", default="segmentation_spleen")
parser.add_argument("-m", "--model", default="deepedit")
parser.add_argument("-t", "--test", default="infer", choices=("train", "infer"))
args = parser.parse_args()

Expand All @@ -308,7 +308,9 @@ def main():

# Run on all devices
for device in device_list():
res = app.infer(request={"model": args.model, "image": image_id, "device": device})
res = app.infer(
request={"model": args.model, "image": image_id, "device": device, "spleen": [[6, 6, 6], [9, 9, 9]]}
)
# res = app.infer(
# request={"model": "vertebra_pipeline", "image": image_id, "device": device, "slicer": False}
# )
Expand Down
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