diff --git a/Docs/developer_guide/script_repository.md b/Docs/developer_guide/script_repository.md
index 395e2f045f0..9a10ba92b26 100644
--- a/Docs/developer_guide/script_repository.md
+++ b/Docs/developer_guide/script_repository.md
@@ -52,3 +52,6 @@ The Slicer source code has Python [scripted modules](https://github.com/Slicer/S
 
 ```{include} script_repository/volumes.md
 ```
+
+```{include} script_repository/batch.md
+```
diff --git a/Docs/developer_guide/script_repository/batch.md b/Docs/developer_guide/script_repository/batch.md
new file mode 100644
index 00000000000..ea0564d3fb5
--- /dev/null
+++ b/Docs/developer_guide/script_repository/batch.md
@@ -0,0 +1,121 @@
+
+## Batch processing
+
+### Iterate through dicom series
+
+This examples shows how to perform an operation on each
+series in the dicom database.
+
+```python
+db = slicer.dicomDatabase
+patients = db.patients()
+patientCount = 0
+for patient in patients:
+  patientCount += 1
+  print(f"Patient {patient} ({patientCount} of {len(patients)})")
+  for study in db.studiesForPatient(patient):
+    print(f"Study {study}")
+    for series in db.seriesForStudy(study):
+      print(f"Series {series}")
+      temporaryDir = qt.QTemporaryDir()
+      for instanceUID in db.instancesForSeries(series):
+        qt.QFile.copy(db.fileForInstance(instanceUID), f"{temporaryDir.path()}/{instanceUID}.dcm")
+      patientID = slicer.dicomDatabase.instanceValue(instanceUID, '0010,0020')
+      outputPath = os.path.join(convertedPath, patientID, study, series, "BatchResult")
+      if not os.path.exists(outputPath):
+        os.makedirs(outputPath)
+      # do an operation here that processes the series into the outputPath
+```
+
+:::{note}
+
+It can be helpful for debugging to include a comment with python commands
+that can be pasted into the console to run the script.  With this approach
+any global variables, such as vtk class instances, defined in the script will exists after the script runs
+and you can easily inspect them or call methods on them.
+
+```python
+"""
+filePath = "/data/myscript.py"
+
+exec(open(filePath).read())
+
+"""
+```
+
+:::
+
+### Extracting volume patches around segments
+
+For machine learning apps, such as [MONAI Label](https://docs.monai.io/projects/label/en/latest/index.html)
+it can be helpful to reduce the size of the problem by
+extracting out subsets of data.  This example shows how to iterate through
+a directory of segmentations, compute their bounding boxes, and save out
+new volumes and segmentations centered around the segmentation.
+
+Here the ROI is aligned with the volume.  See [Segmentations](script_repository.md#segmentations) for
+examples using oriented bounding boxes and other options.
+
+```python
+import glob
+import os
+
+def segROI(segmentationNode):
+  # Compute bounding boxes
+  import SegmentStatistics
+  segStatLogic = SegmentStatistics.SegmentStatisticsLogic()
+  segStatLogic.getParameterNode().SetParameter("Segmentation", segmentationNode.GetID())
+  segStatLogic.getParameterNode().SetParameter("LabelmapSegmentStatisticsPlugin.obb_origin_ras.enabled",str(True))
+  segStatLogic.getParameterNode().SetParameter("LabelmapSegmentStatisticsPlugin.obb_diameter_mm.enabled",str(True))
+  segStatLogic.getParameterNode().SetParameter("LabelmapSegmentStatisticsPlugin.obb_direction_ras_x.enabled",str(True))
+  segStatLogic.getParameterNode().SetParameter("LabelmapSegmentStatisticsPlugin.obb_direction_ras_y.enabled",str(True))
+  segStatLogic.getParameterNode().SetParameter("LabelmapSegmentStatisticsPlugin.obb_direction_ras_z.enabled",str(True))
+  segStatLogic.computeStatistics()
+  stats = segStatLogic.getStatistics()
+
+  # Draw ROI for each oriented bounding box
+  import numpy as np
+  for segmentId in stats["SegmentIDs"]:
+    # Get bounding box
+    obb_origin_ras = np.array(stats[segmentId,"LabelmapSegmentStatisticsPlugin.obb_origin_ras"])
+    obb_diameter_mm = np.array(stats[segmentId,"LabelmapSegmentStatisticsPlugin.obb_diameter_mm"])
+    obb_direction_ras_x = np.array(stats[segmentId,"LabelmapSegmentStatisticsPlugin.obb_direction_ras_x"])
+    obb_direction_ras_y = np.array(stats[segmentId,"LabelmapSegmentStatisticsPlugin.obb_direction_ras_y"])
+    obb_direction_ras_z = np.array(stats[segmentId,"LabelmapSegmentStatisticsPlugin.obb_direction_ras_z"])
+    # Create ROI
+    segment = segmentationNode.GetSegmentation().GetSegment(segmentId)
+    roi=slicer.mrmlScene.AddNewNodeByClass("vtkMRMLMarkupsROINode")
+    roi.SetName(segment.GetName() + " OBB")
+    roi.GetDisplayNode().SetHandlesInteractive(False)  # do not let the user resize the box
+    roi.SetSize(obb_diameter_mm * 2) # make the ROI twice the size of the segmentation
+    # Position and orient ROI using a transform
+    obb_center_ras = obb_origin_ras+0.5*(obb_diameter_mm[0] * obb_direction_ras_x + obb_diameter_mm[1] * obb_direction_ras_y + obb_diameter_mm[2] * obb_direction_ras_z)
+    boundingBoxToRasTransform = np.row_stack((np.column_stack(((1,0,0), (0,1,0), (0,0,1), obb_center_ras)), (0, 0, 0, 1)))
+    boundingBoxToRasTransformMatrix = slicer.util.vtkMatrixFromArray(boundingBoxToRasTransform)
+    roi.SetAndObserveObjectToNodeMatrix(boundingBoxToRasTransformMatrix)
+    return roi
+
+labelFiles = glob.glob("/data/imagesTr/labels/final/*.nii.gz")
+
+for labelFile in labelFiles:
+  slicer.mrmlScene.Clear()
+  print(labelFile)
+  baseName = os.path.basename(labelFile)
+  ctFile = os.path.join("/data/imagesTr", baseName)
+  print(ctFile)
+  ct = slicer.util.loadVolume(ctFile)
+  seg = slicer.util.loadSegmentation(labelFile)
+  roi = segROI(seg)
+  cropVolumeParameters = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLCropVolumeParametersNode")
+  cropVolumeParameters.SetInputVolumeNodeID(ct.GetID())
+  cropVolumeParameters.SetROINodeID(roi.GetID())
+  slicer.modules.cropvolume.logic().Apply(cropVolumeParameters)
+  croppedCT = cropVolumeParameters.GetOutputVolumeNode()
+  seg.SetReferenceImageGeometryParameterFromVolumeNode(croppedCT)
+  segLogic = slicer.modules.segmentations.logic()
+  labelmap = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLLabelMapVolumeNode")
+  segLogic.ExportAllSegmentsToLabelmapNode(seg, labelmap, slicer.vtkSegmentation.EXTENT_REFERENCE_GEOMETRY)
+  slicer.util.saveNode(croppedCT, f"/data/crops/{baseName}")
+  slicer.util.saveNode(labelmap, f"/data/crops/labels/final/{baseName}")
+  slicer.app.processEvents() # to watch progress
+```