Updates: Upgrade MONAI rc3 | Pydantic 1.8.2 | bundle path add | Orthanc Doc (#1086)

Author: tangy5

Dockerfile

@@ -13,7 +13,7 @@
 # please run `./runtests.sh --clean && DOCKER_BUILDKIT=1 docker build -t projectmonai/monailabel:latest .`
 # to use different version of MONAI pass `--build-arg MONAI_IMAGE=...`
 
-ARG MONAI_IMAGE=projectmonai/monai:1.0.1rc2
+ARG MONAI_IMAGE=projectmonai/monai:1.0.1rc3
 ARG BUILD_OHIF=true
 
 FROM ${MONAI_IMAGE} as build

docs/source/quickstart.rst

@@ -273,10 +273,15 @@ endpoint of our DICOM server, which based on the last section is ``http://locaho
   # download DeepEdit sample app to local directory
   monailabel apps --name radiology --download --output .
 
-  # start the DeepEdit app in MONAI label server
+  # For MONAI Label version <=0.2.0, pass credentials and start the DeepEdit app in MONAI label server
   # and start annotating images in our DICOM server
   monailabel start_server --app radiology --studies http://locahost:8042/dicom-web --conf models deepedit --username orthanc --password orthanc
 
+  # For MONAI label version >=0.3.0, if you have authentication set for dicom-web then you can pass the credentials using environment
+  #`variables <https://github.com/Project-MONAI/MONAILabel/blob/main/monailabel/config.py>`_ while running the server.
+  export MONAI_LABEL_DICOMWEB_USERNAME=xyz
+  export MONAI_LABEL_DICOMWEB_PASSWORD=abc
+  monailabel start_server --app apps/radiology --studies http://127.0.0.1:8042/dicom-web --conf models deepedit
 
 At this point OHIF can be used to annotate the data in the DICOM server via the MONAI Label server ``/ohif`` endpoint
 (e.g. via `http://127.0.0.1:8000/ohif <http://127.0.0.1:8000/ohif>`_).
@@ -424,11 +429,8 @@ Prerequisite: Check Model Zoo `Release <https://github.com/Project-MONAI/model-z
   # Step 3: download a local study images, sample dataset such as spleen CT (contrast enhanced CTs are better):
   monailabel datasets --download --name Task09_Spleen --output .
 
-  # Step4: set customised bundle scripts to python path (the bundle models and code are downloaded when start monai label server)
-  export PYTHONPATH=$PYTHONPATH:"monaibundle/model/renalStructures_UNEST_segmentation_v0.2.0"
-
-  # Step 5: start the bundle app in MONAI label server
-    monailabel start_server --app monaibundle --studies Task09_Spleen/imagesTr --conf models renalStructures_UNEST_segmentation_v0.2.0
+  # Step 4: start the bundle app in MONAI label server
+  monailabel start_server --app monaibundle --studies Task09_Spleen/imagesTr --conf models renalStructures_UNEST_segmentation_v0.2.0
 
 
 

requirements.txt

@@ -10,9 +10,9 @@
 # limitations under the License.
 
 torch>=1.7
-monai[nibabel, skimage, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil, openslide, fire]>=1.0.1rc2
+monai[nibabel, skimage, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil, openslide, fire]>=1.0.1rc3
 uvicorn==0.17.6
-pydantic==1.9.1
+pydantic>=1.8.2
 python-dotenv==0.20.0
 fastapi==0.78.0
 python-multipart==0.0.5

sample-apps/monaibundle/main.py

@@ -13,6 +13,7 @@
 import os
 import re
 import shutil
+import sys
 from typing import Dict
 
 import requests
@@ -80,6 +81,7 @@ def __init__(self, app_dir, studies, conf):
                     e = os.path.join(self.model_dir, re.sub(r"_v.*.zip", "", f"{k}.zip"))
                     if os.path.isdir(e):
                         shutil.move(e, p)
+            sys.path.append(p)
 
             self.models[k] = p
 

setup.cfg

@@ -36,9 +36,9 @@ setup_requires =
     ninja
 install_requires =
     torch>=1.7
-    monai[nibabel, skimage, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil, openslide, fire]>=1.0.1rc2
+    monai[nibabel, skimage, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil, openslide, fire]>=1.0.1rc3
     uvicorn==0.17.6
-    pydantic==1.9.1
+    pydantic>=1.8.2
     python-dotenv==0.20.0
     fastapi==0.78.0
     python-multipart==0.0.5