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Fixed measurement group tracking UID to make it work with OHIF

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Aljaž Gerečnik 2025-02-15 10:21:23 +01:00
commit 1ac99bdcfa
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*~
OrthancStorage/
__pycache__/
config.json
venv/
# Those files are automatically downloaded
models/resnet50-11ad3fa6.pth
models/retina_res50_trained_08_03.pth

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cff-version: "1.1.0"
message: "If you use this software, please cite it using these metadata."
title: Orthanc
abstract: "Orthanc is a lightweight open-source DICOM server for medical imaging supporting representational state transfer (REST)."
authors:
-
affiliation: UCLouvain
family-names: Jodogne
given-names: "Sébastien"
doi: "10.1007/s10278-018-0082-y"
license: "GPL-3.0-or-later"
repository-code: "https://orthanc.uclouvain.be/hg/orthanc/"
version: 1.12.3
date-released: 2024-01-31

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interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<http://www.gnu.org/licenses/>.

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/**
* Orthanc plugin for mammography
* Copyright (C) 2024 Edouard Chatzopoulos and Sebastien Jodogne,
* ICTEAM UCLouvain, Belgium
*
* This program is free software: you can redistribute it and/or
* modify it under the terms of the GNU Affero General Public License
* as published by the Free Software Foundation, either version 3 of
* the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
**/
var mammographyButton = null;
$('#instance').live('pagecreate', function() {
mammographyButton = $('<a>')
.attr('data-role', 'button')
.attr('href', '#')
.attr('data-icon', 'search')
.attr('data-theme', 'e')
.text('Deep learning for mammography');
mammographyButton.insertBefore($('#instance-delete').parent().parent());
mammographyButton.click(function() {
if ($.mobile.pageData) {
$.ajax({
type: 'POST',
url: '../mammography-apply',
dataType: 'json',
contentType: 'application/json',
data: JSON.stringify({
'instance' : $.mobile.pageData.uuid,
}),
cache: false,
success: function(result) {
console.log(result);
window.location.assign('explorer.html#series?uuid=' + result.ParentSeries);
},
error: function() {
alert('Cannot apply deep learning model');
}
});
}
});
});
$('#instance').live('pagebeforeshow', function() {
mammographyButton.hide();
});
$('#instance').live('pageshow', function() {
$.ajax({
url: '../instances/' + $.mobile.pageData.uuid + '/tags?simplify',
dataType: 'json',
cache: false,
success: function(tags) {
if (tags.Modality == 'MG') {
mammographyButton.show();
}
}
});
});
$('#study').live('pagebeforecreate', function() {
var b = $('<a>')
.attr('data-role', 'button')
.attr('href', '#')
.attr('data-icon', 'search')
.attr('data-theme', 'e')
.text('Stone Web Viewer (for mammography)');
b.insertBefore($('#study-delete').parent().parent());
b.click(function() {
if ($.mobile.pageData) {
$.ajax({
url: '../studies/' + $.mobile.pageData.uuid,
dataType: 'json',
cache: false,
success: function(study) {
var studyInstanceUid = study.MainDicomTags.StudyInstanceUID;
window.open('../mammography-viewer/index.html?study=' + studyInstanceUid);
}
});
}
});
});
$('#series').live('pagebeforecreate', function() {
var b = $('<a>')
.attr('data-role', 'button')
.attr('href', '#')
.attr('data-icon', 'search')
.attr('data-theme', 'e')
.text('Stone Web Viewer (for mammography)');
b.insertBefore($('#series-delete').parent().parent());
b.click(function() {
if ($.mobile.pageData) {
$.ajax({
url: '../series/' + $.mobile.pageData.uuid,
dataType: 'json',
cache: false,
success: function(series) {
$.ajax({
url: '../studies/' + series.ParentStudy,
dataType: 'json',
cache: false,
success: function(study) {
var studyInstanceUid = study.MainDicomTags.StudyInstanceUID;
var seriesInstanceUid = series.MainDicomTags.SeriesInstanceUID;
window.open('../mammography-viewer/index.html?study=' + studyInstanceUid +
'&series=' + seriesInstanceUid);
}
});
}
});
}
});
});

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Mammography plugin for Orthanc
==============================
This plugin for [Orthanc](https://orthanc.uclouvain.be/) adds a **deep
learning algorithm to detect masses in scanned mammograms**. The inference
algorithm produces DICOM-SR (structured reports) instances that are
stored inside Orthanc. The DICOM-SR instance can then be displayed
using a version of the [Stone Web viewer for
Orthanc](https://www.orthanc-server.com/static.php?page=stone-web-viewer).
[Reference paper](https://ebooks.iospress.nl/doi/10.3233/SHTI240603),
in the BibTeX format:
```BibTeX
@inproceedings{OrthancMIE2024,
title = {Integrated and Interoperable Platform for Detecting Masses on Mammograms},
author = {Chatzopoulos, Edouard and Jodogne, S\'ebastien},
booktitle = {34th Medical Informatics Europe Conference (MIE 2024)},
address = {Athens, Greece},
journal = {Studies in Health Technology and Informatics},
volume = {316},
pages = {1103-1107},
issn = {1879-8365},
doi = {10.3233/SHTI240603},
publisher = {{IOS} Press},
year = {2024}
}
```
This plugin is copyrighted by the UCLouvain university, under the
AGPLv3+ license. It is a deliverable of the research lab led by
[Sébastien Jodogne](https://info.ucl.ac.be/~sjodogne/).
![Example](viewer/2024-03-09-FirstDicomSR.png)
Setup
-----
Make sure that Python 3 is installed with support of [virtual
environments](https://docs.python.org/3/library/venv.html). Under
Debian/Ubuntu distributions, this can be done by running:
```
$ sudo apt update
$ sudo apt install python3 python3-venv
```
Evidently, you also have to [install
Orthanc](https://www.orthanc-server.com/download.php). The mammography
plugin requires the two following plugins for Orthanc to be installed:
* [Python plugin](https://orthanc.uclouvain.be/book/plugins/python.html).
* [DICOMweb plugin](https://orthanc.uclouvain.be/book/plugins/dicomweb.html), if you want to use the DICOM-SR viewer.
Configuration
-------------
Firstly, clone this repository:
```
$ cd /tmp/
$ git clone https://github.com/jodogne/orthanc-mammography/
```
Secondly, setup the Python virtual environment with the required
dependencies:
```
$ cd /tmp/orthanc-mammography/
$ python3 -m venv venv
$ source ./venv/bin/activate
(venv) python3 -m pip install --upgrade pip
(venv) python3 -m pip install -r requirements.txt
```
Thirdly, create a [configuration
file](https://orthanc.uclouvain.be/book/plugins/dicomweb.html#installation)
for Orthanc with Python. A minimalist configuration file would look
like (make sure to adapt the path to the virtual environment,
depending on your version of Python):
```json
{
"Plugins" : [
"...list of paths to the Python and DICOMweb plugins..."
],
"Python" : {
"Path" : "./mammography.py"
},
"Mammography" : {
"VirtualEnv" : "./venv/lib/python3.10/site-packages/"
}
}
```
Finally, start Orthanc using this configuration file.
Usage
-----
Two samples images from the [CBIS-DDSM
dataset](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=22516629)
are available in the [`./sample-images/` folder](./sample-images/).
[Upload DICOM images of mammograms to
Orthanc](https://orthanc.uclouvain.be/book/users/cookbook.html#uploading-dicom-files).
Using the built-in [Orthanc
Explorer](http://localhost:8042/app/explorer.html) interface of
Orthanc, go down in the DICOM hierarchy until you reach a DICOM
instance whose modality is `MG` (mammogram). The yellow button `Deep
learning for mammography` will appear.
Clicking on the button will launch the deep learning inference. Once
the inference is done (this takes about 5 seconds on a standard
laptop), Orthanc Explorer is automatically redirected to the
newly-created DICOM series that contains the DICOM-SR instance with
the results of the inference.
You can then click on the yellow button `Stone Web viewer (for
mammography)` to display the content of the DICOM-SR.

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# Orthanc plugin for mammography
# Copyright (C) 2024 Edouard Chatzopoulos and Sebastien Jodogne,
# ICTEAM UCLouvain, Belgium
#
# This program is free software: you can redistribute it and/or
# modify it under the terms of the GNU Affero General Public License
# as published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import highdicom
import model
import numpy
import pydicom
import time
from datetime import datetime
def CreateProbabilityOfCancer(probability):
assert(probability >= 0 and probability <= 100)
return [
highdicom.sr.Measurement(
name = pydicom.sr.codedict.codes.DCM.ProbabilityOfCancer,
value = probability,
unit = pydicom.sr.codedict.codes.UCUM.Percent,
),
]
def apply(retina_net, dicom,
minimum_score = 0.2,
title = 'Orthanc Deep Learning for Mammography'):
start = time.time()
result = model.apply_model_to_dicom(retina_net, dicom, rescale_boxes=True)
end = time.time()
print('Time: %.02f seconds' % (end - start))
assert(len(result['boxes']) == len(result['scores']))
# Generic X-ray report, added in DICOM 2023c, so not directly
# available in pydicom and in DicomSRValidator:
# https://dicom.nema.org/medical/dicom/2023c/output/chtml/part16/sect_CID_100.html
reportedProcedure = pydicom.sr.coding.Code(value = '43468-8',
scheme_designator = 'LN',
meaning = 'XR unspecified body region')
observation_context = highdicom.sr.ObservationContext()
planar_groups = []
for i in range(len(result['boxes'])):
score = result['scores'][i].detach().numpy()
if score > minimum_score:
box = result['boxes'][i].detach().numpy()
assert(len(box) == 4)
(x1, y1, x2, y2) = box
planar_groups.append(highdicom.sr.PlanarROIMeasurementsAndQualitativeEvaluations(
referenced_region = highdicom.sr.ImageRegion(
graphic_type = highdicom.sr.GraphicTypeValues.POLYLINE,
graphic_data = numpy.array([ [x1, y1], [x2, y1], [x2, y2], [x1,y2], [x1,y1] ]),
source_image = highdicom.sr.SourceImageForRegion.from_source_image(dicom, referenced_frame_numbers = None),
),
tracking_identifier = highdicom.sr.TrackingIdentifier(
identifier = title,
uid = highdicom.UID(),
),
# 'SCT' means 'SNOMED-CT'
# geometric_purpose = highdicom.sr.CodedConcept('75958009', 'SCT', 'Bounded by'),
measurements = CreateProbabilityOfCancer(score * 100.0),
))
measurement_report = highdicom.sr.MeasurementReport(
observation_context = observation_context,
procedure_reported = reportedProcedure,
imaging_measurements = planar_groups,
)
sr_object = highdicom.sr.ComprehensiveSR(
evidence = [ dicom ],
content = measurement_report,
series_number = 1,
series_instance_uid = highdicom.UID(),
sop_instance_uid = highdicom.UID(),
instance_number = 1
)
sr_object.SeriesDate = datetime.now().strftime("%Y%m%d")
sr_object.SeriesTime = datetime.now().strftime("%H%M%S")
return sr_object

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# Orthanc plugin for mammography
# Copyright (C) 2024 Edouard Chatzopoulos and Sebastien Jodogne,
# ICTEAM UCLouvain, Belgium
#
# This program is free software: you can redistribute it and/or
# modify it under the terms of the GNU Affero General Public License
# as published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import hashlib
import io
import os
import requests
import sys
def get(target, url, expected_size, expected_md5):
print('Downloading: %s' % url)
if os.path.isfile(target):
with open(target, 'rb') as f:
content = f.read()
if (len(content) == expected_size and
hashlib.md5(content).hexdigest() == expected_md5):
print(' File already downloaded')
return
else:
raise Exception('Target file already exists with bad content, please remove it: %s' % target)
r = requests.get(url, stream = True)
r.raise_for_status()
content_length = int(r.headers['Content-Length'])
if content_length != expected_size:
raise Exception('Bad file size on the server: %d (actual) vs. %d (expected)' % (content_length, expected_size))
bar_width = 30
with io.BytesIO() as f:
progress = 0
for chunk in r.iter_content(chunk_size = 1024 * 1024):
f.write(chunk)
progress += len(chunk)
percent = float(progress) / float(content_length)
sys.stdout.write('\r')
sys.stdout.write(' Completed: [{:{}}] {:>3}%'
.format('=' * round(percent * float(bar_width)),
bar_width, round(percent * 100.0)))
sys.stdout.flush()
sys.stdout.write('\n')
sys.stdout.flush()
f.seek(0)
content = f.read()
if len(content) != expected_size:
raise Exception('Server has not returned the expected number of bytes: %d (actual) vs. %d (expected)' % (len(content), expected_size))
actual_md5 = hashlib.md5(content).hexdigest()
if actual_md5 != expected_md5:
raise Exception('Bad MD5 checksum: %s (actual) vs. %s (expected)' % (actual_md5, expected_md5))
with open(target, 'wb') as f:
f.write(content)

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# Orthanc plugin for mammography
# Copyright (C) 2024 Edouard Chatzopoulos and Sebastien Jodogne,
# ICTEAM UCLouvain, Belgium
#
# This program is free software: you can redistribute it and/or
# modify it under the terms of the GNU Affero General Public License
# as published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
##
## Initialize the virtual environment for pytorch.
## DO NOT add other "import" in this section!
##
import sys
import json
import orthanc
config = json.loads(orthanc.GetConfiguration()).get('Mammography', {})
venv = config.get('VirtualEnv')
if venv != None:
# https://orthanc.uclouvain.be/book/plugins/python.html#working-with-virtual-environments
sys.path.insert(0, venv)
##
## Install the Stone Web viewer
##
STONE_VERSION = '2024-08-31-StoneWebViewer-DICOM-SR'
VIEWER_PREFIX = '/mammography-viewer/'
import os
SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
VIEWER_DIR = os.path.join(SCRIPT_DIR, 'viewer')
sys.path.append(os.path.join(SCRIPT_DIR, '..'))
import download
os.makedirs(VIEWER_DIR, exist_ok = True)
download.get(os.path.join(VIEWER_DIR, '%s.zip' % STONE_VERSION),
'https://github.com/jodogne/orthanc-mammography/raw/master/viewer/%s.zip' % STONE_VERSION,
4815178, '86b52a17f86e4769d12e9ae680c4a99f')
import zipfile
stone_assets = zipfile.ZipFile(os.path.join(VIEWER_DIR, '%s.zip' % STONE_VERSION))
MIME_TYPES = {
'.css' : 'text/css',
'.gif' : 'image/gif',
'.html' : 'text/html',
'.jpeg' : 'image/jpeg',
'.js' : 'text/javascript',
'.png' : 'image/png',
}
def serve_stone_web_viewer(output, uri, **request):
if not uri.startswith(VIEWER_PREFIX):
output.SendHttpStatusCode(404)
elif request['method'] != 'GET':
output.SendMethodNotAllowed('GET')
else:
try:
path = '%s/%s' % (STONE_VERSION, uri[len(VIEWER_PREFIX):])
extension = os.path.splitext(path) [1]
if not extension in MIME_TYPES:
mime = 'application/octet-stream'
else:
mime = MIME_TYPES[extension]
with stone_assets.open(path) as f:
output.AnswerBuffer(f.read(), mime)
except:
output.SendHttpStatusCode(500)
orthanc.RegisterRestCallback('%s(.*)' % VIEWER_PREFIX, serve_stone_web_viewer)
##
## Load the deep learning model
##
import highdicom
import io
import os
import pydicom
sys.path.append(os.path.join(SCRIPT_DIR, '..'))
import model
import dicom_sr
orthanc.LogWarning('Loading the RetinaNet model for mammography')
my_retina_net = model.load_retina_net()
##
## Install the Orthanc Explorer extension
##
with open(os.path.join(SCRIPT_DIR, 'OrthancExplorer.js'), 'r') as f:
orthanc.ExtendOrthancExplorer(f.read())
def execute_inference(output, uri, **request):
if request['method'] != 'POST':
output.SendMethodNotAllowed('POST')
else:
body = json.loads(request['body'])
f = orthanc.GetDicomForInstance(body['instance'])
dicom = pydicom.dcmread(io.BytesIO(f))
if len(dicom.pixel_array.shape) != 2:
orthanc.LogError('Not a graylevel instance: %s' % body['instance'])
output.SendHttpStatusCode(400)
else:
result = dicom_sr.apply(my_retina_net, dicom, minimum_score=0.2)
with io.BytesIO() as f:
pydicom.dcmwrite(f, result)
f.seek(0)
content = f.read()
output.AnswerBuffer(orthanc.RestApiPost('/instances', content), 'application/json')
orthanc.RegisterRestCallback('/mammography-apply', execute_inference)

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# Orthanc plugin for mammography
# Copyright (C) 2024 Edouard Chatzopoulos and Sebastien Jodogne,
# ICTEAM UCLouvain, Belgium
#
# This program is free software: you can redistribute it and/or
# modify it under the terms of the GNU Affero General Public License
# as published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import download
import os
import numpy as np
import torch
import torch.nn as nn
import torchvision.transforms.v2 as transforms
from functools import partial
from torchvision.models.detection import RetinaNet
from torchvision.models.detection.anchor_utils import AnchorGenerator
from torchvision.models.detection.backbone_utils import _resnet_fpn_extractor
from torchvision.models.detection.retinanet import RetinaNetHead
from torchvision.models.resnet import resnet50, ResNet50_Weights
from torchvision.ops import FrozenBatchNorm2d
from torchvision.ops.feature_pyramid_network import LastLevelP6P7
SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
MODELS_DIR = os.path.join(SCRIPT_DIR, 'models')
os.makedirs(MODELS_DIR, exist_ok = True)
download.get(os.path.join(MODELS_DIR, 'resnet50-11ad3fa6.pth'),
'https://orthanc.uclouvain.be/downloads/cross-platform/orthanc-mammography/models/2024-03-08-resnet50-11ad3fa6.pth',
102540417, '012571d812f34f8442473d8b827077b5')
download.get(os.path.join(MODELS_DIR, 'retina_res50_trained_08_03.pth'),
'https://orthanc.uclouvain.be/downloads/cross-platform/orthanc-mammography/models/2024-03-08-retina_res50_trained_08_03.pth',
145735292, '53aa159ea0b83234d767aacb43619748')
class ResizeBetter:
def __init__(self, min_size=1750):
self.min_size = min_size
def __call__(self, sample):
shape = sample[0].shape[-2:]
return transforms.Resize((self.min_size,int(self.min_size*shape[1]/shape[0])),
interpolation=transforms.InterpolationMode.BILINEAR,
antialias=True)(sample)
def anchorgen():
anchor_sizes = tuple((x, int(x * 2 ** (1.0 / 3)), int(x * 2 ** (2.0 / 3))) for x in [32, 64, 128, 256, 512])
aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios)
return anchor_generator
def load_model(config, pretrained_path, mean=0, std=1):
if False:
# Download backbone from Internet
model_backbone = resnet50(norm_layer = FrozenBatchNorm2d,
weights=ResNet50_Weights.DEFAULT)
else:
weights = torch.load(os.path.join(MODELS_DIR, 'resnet50-11ad3fa6.pth'), map_location=torch.device('cpu'))
model_backbone = resnet50(norm_layer = FrozenBatchNorm2d)
model_backbone.load_state_dict(weights)
model_backbone.fc = model_backbone.fc = nn.Sequential(
nn.Linear(4*512 , config["num_classes"])
)
model_backbone = _resnet_fpn_extractor(model_backbone,
config["trainable_backbone_layers"],
returned_layers=[2, 3, 4],
extra_blocks=LastLevelP6P7(2048, 256))
anchor_generator = anchorgen()
head = RetinaNetHead(
model_backbone.out_channels,
anchor_generator.num_anchors_per_location()[0],
config["num_classes"],
norm_layer=partial(nn.GroupNorm, 32),
)
head.regression_head._loss_type = "giou"
model = RetinaNet(model_backbone,
num_classes=config["num_classes"],
anchor_generator=anchor_generator,
head=head,
min_size=config["min_size"] ,
max_size=config["max_size"],
image_mean=[mean, mean, mean],
image_std=[std, std, std],
fg_iou_thresh=config["fg_iou_thresh"],
bg_iou_thresh=config["bg_iou_thresh"],
nms_thresh=config["nms_thresh"],
_skip_resize=True)
if pretrained_path is not None:
state_dict = torch.load(pretrained_path, map_location=torch.device('cpu'))
model.load_state_dict(state_dict)
print(f"Model loaded from checkpoint {pretrained_path}")
for layer in model.modules():
if isinstance(layer, nn.BatchNorm2d) or isinstance(layer, nn.BatchNorm1d):
layer.eval() # Set to evaluation mode
layer.weight.requires_grad = False
layer.bias.requires_grad = False
model.eval()
return model
def load_retina_net():
config = {
'num_classes' : 2,
'min_size' : 2048,
'max_size' : 2048,
'trainable_backbone_layers' : 0,
'fg_iou_thresh' : 0.5,
'bg_iou_thresh' : 0.4,
'nms_thresh' : 0.3,
}
return {
'min_size' : config['min_size'],
'eval' : load_model(config, os.path.join(MODELS_DIR, 'retina_res50_trained_08_03.pth')),
}
def dicom_to_tensor(dicom, min_size):
assert(len(dicom.pixel_array.shape) == 2)
#Normalize the value scale to 0-255 (Useful for some processing steps)
im_array = np.stack((dicom.pixel_array,)*3, axis=-1)
im_max = np.max(im_array)
im_min = np.min(im_array)
image_tensor = torch.tensor(im_array.astype(np.float32).transpose(2, 0, 1))
#Resize longest side to 2048 (with same ratio) and normalize
image_tensor = ResizeBetter(min_size) (image_tensor)
std = torch.std(image_tensor)
mean = torch.mean(image_tensor)
image_tensor = torch.sub(image_tensor, mean )
image_tensor = torch.div(image_tensor, std)
assert(len(image_tensor.shape) == 3)
return image_tensor
def apply_model_to_dicom(model, dicom, rescale_boxes=True):
image_tensor = dicom_to_tensor(dicom, model['min_size'])
output = model['eval'] ([ image_tensor ])
assert(len(output) == 1)
output = output[0]
if rescale_boxes:
originalWidth = dicom.pixel_array.shape[1]
originalHeight = dicom.pixel_array.shape[0]
resizedWidth = image_tensor.shape[2]
resizedHeight = image_tensor.shape[1]
# TODO - The "int()" in ResizeBetter is anisotropic
ratio = originalWidth / resizedWidth
output['boxes'] *= ratio
return output

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highdicom==0.22.0
numpy==2.1.0
opencv-python==4.10.0.84
pydicom==2.4.4
torch==2.3.0
torchaudio==2.3.0
torchvision==0.18.0

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