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SAR 2020 - AI Masters Class

Class Schedule

Date / Time Location
Sun 3/1 2:00-4:00 pm Hibiscus Room

Slides

Requirements

All lessons are designed to run in Google Colab, which is a free web-based version of Jupyter hosted by Google. You will need a Google account (eg, gmail) to use Colab. If you don't already have a Google account, please create one in advance at the account sign-up page. You can delete the account when you complete the lessons if you wish.

Lesson Notebooks

Notebooks on Intro to Python, DICOM Basics, Classification, and Segmentation for AI Masters Class

Lesson 01 - Intro to Python

Very basic introduction to Python and the Jupyter/Colab environment. If you've never visited the command line, start here.

Lesson 02 - DICOM Basics

Brief introduction to reading DICOM files including information from the DICOM header and the pixel array. Relies on pydicom and concepts from fastaiv2 medical imaging libraries.

Lesson 03 - Classification

Build a simple image classifier to identify a chest x-ray as either frontal or lateral projection. Based on fastai library and Deep Learning for Coders lesson.

Lesson 04 - Segmentation

Adapts Facebook Research's detectron2 library to read native DICOM and segment the liver on CT scans. We'll feed single-channel full bit-depth Hounsfield unit pixel data into detectron2 - DICOM and segmentations are from the recent Combined Healthy Abdominal Organ Segmentation (CHAOS) grand challenge.

Key modifications to detectron2 to enable reading DICOM natively:

  • Custom data mapper to read the dcm file pixel_array and convert to Houndfield units.

    • Applies random brightness and random contrast run time augmentations.
    • Does NOT apply random flipping as horizontal flipping seems to lead to some liver-spleen confusion for the algorithm.
    • Sets mask type to "bitmask" from default "polygons"
  • Custom get_liver_dicts referenced by DatasetCatalog.register function

    • Loads from _train, _val, or _test file lists
    • Saves path to .dcm file as dataset_dict["file_name"]
      • Default detectron data mapper simply opens the image file from file_name. Our custom data mapper will instead read the dcm pixel array, convert to Hounsfield units, and load the image array from that
    • Gets image height and width from dcm.pixel_array
    • Creates bounding box automatically from the given mask (via separate bbox function)
    • Converts binary PNG mask to RLE format using pycocotools library
  • Modifies base configuration file for the trainer

    • cfg.SOLVER.IMS_PER_BATCH, BASE_LR, and MAX_ITER are good places to start tweaking to modify performance
    • cfg.INPUT.FORMAT = "F" tells the model to expect single channel float array instead of default "RGB"
    • cfg.INPUT.MASK_FORMAT = "bitmask" change from default "polygons"
  • Extends the DefaultTrainer as LiverTrainer

    • To use custom data mapper in the build_train_loader and build_test_loader functions.
    • To use custom config file with modifications as above
  • Replaces the DefaultPredictor class with LiverPredictor

    • Change torch.as_tensor to use single channel input
  • Note that windowing is only done when displaying example images or overlaid inference results

    • Pixel data that are loaded into the network are NOT windowed since that would needlessly compress the dynamic range to the (presumed) detriment of the model's learning.
      • In this way windowing in python/ML applications means something slightly different than windowing at a diagnostic workstation. Radiolgists know but may not fully appreciate (and do not seem to communicate to computer scientists) that windowing at the workstation doesn't change the actual pixel values - an ROI will be the same regardless of the window/level setting applied since it is measuring the true HU, not the screen's pixel greyscale values. Windowing in python/ML, on the other hand, fundamentally changes the underlying pixel values.

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