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Automatic multi-object organ detection and segmentation in abdominal CT data

The ability to generate 3D patient models in a fast and reliable way, is of great importance, e.g. for the simulation of liver punctures in virtual reality simulations. The aim is to automatically detect and segment abdominal structures in CT scans. In particular in the selected organ group, the pancreas poses a challenge. We use a combination of random regression forests and 2D U-Nets to detect bounding boxes and generate segmentation masks for five abdominal organs (liver, kidneys, spleen, pancreas). Training and testing is carried out on 50 CT scans from various public sources. The results show Dice coefficients of up to 0.71. The proposed method can theoretically be used for any anatomical structure, as long as sufficient training data is available.