Residual-based semantic segmentation for bitewing radiograph annotation - Penaflorida, Carlos Daniel S. (Quezon City College of Engineering, University of the Philippines Diliman, 2017)
Current analysis of dental bitewing radiographs are mostly done manually making it time-consuming, subjective and poorly-reproducible. A method for automatically la- belling radiographs is proposed to address this reliance in human intervention and, in doing so, create more reliable dental analyses. The proposed method performs seman tic image segmentation by passing images to a neural network which does pixelwise classification. Results show that the highest performing architecture uses the residual reformulation for feature extraction, nonlinear upsampling for image reconstruction. The latter, however, is dependent on the average training image size used. More so, the network's usage of a label is heavily influenced by the said label's frequency in the training dataset. Finally, while dataset augmentation using 200x200-sized, cropped translated images were able to fill the two said needs, it barely makes the network robust, leading to a bounded average accuracy.
With user manual (bound with the main text).
Thesis (B.S. Computer Science)--University of the Philippines Diliman
Keywords: Teeth -- Radiography