9 Computer Vision News Computer Vision News Leveraging Probabilistic Segmentation … In this work, Anna and Paul focus on the uncertainty involved in segmenting two areas in the eye: the optic cup and the optic disc. Segmenting these areas is crucial for diagnosing glaucoma, where the optic cup, a smaller part within the optic disc, enlarges. “Even experts very often disagree when they’re segmenting these areas,” Anna tells us. “We wanted to model this uncertainty with a probabilistic model and then propagate this learned uncertainty through a pipeline that is very close to clinical practice to then ultimately predict glaucoma for a patient.” With this probabilistic modeling of the uncertainty, the team compares different probabilistic models on how well they perform. They also propose a new feature to predict glaucoma extracted from the segmentation: the rim thickness curve (RTC). The RTC is the distance between the disc and the cup for every point. As the optic cup is bigger in glaucoma patients, a thinner rim suggests glaucoma. While doctors use various diagnostic tools, this method is an excellent first screening approach. From a machine learning perspective, predicting glaucoma directly from images might seem more straightforward; however, the team found that this pipeline, which mirrors clinical workflows, performs better. “The challenge here was to find a way to retain performance but also make it more accessible to clinicians so that they trust the predictions because it’s close to how they work,” Paul explains. “That’s the challenge we faced at the beginning of this problem. How do we get doctors to trust this mechanism that we have?”
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