#U-Net
5 resultsSparse Annotations with Random Walks for U-Net Segmentation of Biodegradable Bone Implants in Synchrotron Microtomograms
Niclas Bockelmann, Diana Krüger, D. C. Florian Wieland +9
The paper introduces a random-walk based method to generate sparse annotations for training a U‑Net to segment biodegradable bone implants in synchrotron microtomography, achieving…
Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers
Yngve Mardal Moe, Aurora Rosvoll Groendahl, Martine Mulstad +5
The paper presents a U‑Net based convolutional neural network that automatically segments gross tumour volume and pathological lymph nodes in head‑and‑neck PET/CT scans, achieving…
Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma
Roxane Licandro, Johannes Hofmanninger, Matthias Perkonigg +7
The paper introduces an asymmetric cascade network composed of two U‑Nets to predict future bone lesions in smoldering multiple myeloma from longitudinal whole‑body T1‑weighted MRI…
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
Jiaming Liu, Chi-Hao Wu, Yuzhi Wang +8
The paper proposes a Bayer pattern unification method and a Bayer-preserving augmentation technique to enable effective deep learning-based denoising of raw sensor images, achievin…
Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks
Ningning Zhao, Nuo Tong, Dan Ruan +1
The paper presents a fully automated two‑stage 3D U‑Net framework for segmenting the pancreas in CT images, achieving a mean Dice score of 85.99% on the NIH dataset.