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Localising Organs of the Fetus in MRI Data Using Python


What if medical scanners could localise organs in 3D data in the same way as your camera automatically detects faces? The task is even more challenging for a fetus, whose orientation is arbitrary! In this talk, I will present how scikit-learn's Random Forest and the Python ecosystem can be used to solve this problem, wrapping a medical imaging C++ library with Cython along the way.


Imaging the developing fetus

The main challenge when imaging the fetus using Magnetic Resonance Imaging (MRI) is fetal movement. Acquiring MR images requires time, during which the fetus is unlikely to stay still. To address this problem, images are acquired as stacks of 2D slices that freeze in-plane motion and motion that occurred between slices is corrected retrospectively. Several stacks of 2D slices acquired in orthogonal directions are used to reconstruct a high resolution volume of the fetus. The automated detection of the fetal organs can help this problem by selecting a region of interest around parts of the fetal body that move as a rigid body. In this talk, I will present the pipeline I developed to localise the brain, heart, lungs and liver of the fetus in MR images, which can be used as pre-processing step for motion correction.

Localising the brain of the fetus

The brain is first detected in each 2D slice using a Bag-of-Words approach with SIFT features, before accumulating the detection results with RANSAC in order to position a 3D bounding box around the brain. This bounding box is later refined into a segmentation, which is used for motion correction. The code, an example dataset and the associated publications can be found online [1].

The OpenCV Python wrapper is used for feature extraction, while an SVM from scikit-learn takes care of the machine learning aspect of the Bag-of-Words. Scikit-learn's Random Forests, trained with patches from the central region of the brain bounding box and patches outside the bounding box, are used to refine the box detection into a segmentation. Medical images combine voxel data with scanner coordinates, which are used to align successive scans to each other. In order to correctly handle these coordinates throughout the Python code, as well as easily access medical image processing functionalities, a wrapper for the IRTK library [2] was developed using Cython. An image object is a subclass of numpy arrays. In particular, the function __getitem__ is overloaded in order to update the scanner coordinates each time the image is cropped or sliced.

Localising the heart, lungs and liver of the fetus

The location of the brain can be used to guide the search for other organs of the fetus. The heart, lungs and liver are detected using a two-step Random Forest approach: a first classification step assigns each voxel to an organ, and during a subsequent regression step, voxels vote for the position of the organ center. Steerable cube features, which can be efficiently computed using integral images, are used as input for the Random Forests. While all fetuses are aligned at training time, at test time image features are extracted in a coordinate system estimated as organs are detected: first the brain, which fixes a point, then the heart, which fixes an axis, and finally the liver and both lungs.

Scikit-learn's Random Forests are not designed for parsing images. They can however be used in a research prototype by precomputing features for batches of voxels. This approach allows the researcher to focus on the detection pipeline and the feature extraction step. Example localisation results are presented in an online video [3].


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