In my first post on the topic, I’ll try to pretend I know what I’m talking about and highlight papers I’ve found interesting in the medical image segmentation field. They are all from Q3 and Q4 of 2017 and work on different architectures. This field of research has several branches as you might suppose and I’m currently most interested in two topics: liver segmentation of CT/MRI images, as well as feature extraction of ultrasound images (a good example and combination of CT and ultrasound combinations can be found in Milletari et al.). However I mostly focus on the first topic, which finds itself on the same floor as other organ segmentation problems, as well as organ substructure segmentation. My main goal is not diagnosis through algorithm but AR/VR visualisation of the images. It’s not an obvious problem: different organs have different sizes and shapes, different “boundaries” or “walls” and the resolution of CT/MR images is not exactly 4K, but more like sub-1000x1000. Even more than in other machine learning fields, the available datasets are really really small still — it is absolutely normal to see datasets of 30 to 40 images instead of people using for example the bigger LiTS dataset — so the anatomical variations between humans can really affect the results. Clearly, research would benefit from pooling together those kinds of images into one big anonymised, labeled and open-source European dataset. But back to the papers!