Adaptable Semi-Automated 3D Segmentation Using Deep Learning with Spatial Slice Propagation

University essay from KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

Abstract: Even with the recent advances of deep learning pushing the field of medical image analysis further than ever before, progress is still slow due to limited availability of annotated data. There are multiple reasons for this, but perhaps the most prominent one is the amount of time manual annotation of medical images takes. In this project a semi-automated algorithm is proposed, approaching the segmentation problem in a slice by slice manner utilising the prediction of a previous slice as a prior for the next. This both allows the algorithm to segment entirely new cases and gives the user the ability to correct faulty slices, propagating the correction throughout. Results on par with current state of the art is achieved within the domain of the training data. In addition to this, cases outside of the training domain can also be segmented with some accuracy, paving the way for further improvement. The strategy for training the network to utilise auxiliary input lies in the heavy online data augmentation, forcing the network to rely on the provided prior.

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