MedGAN: Medical Image Translation using GANs

Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches.Comment: 16 pages, 8 figure

Artifact Correction and Real-Time Scatter Estimation for X-Ray Computed Tomography in Industrial Metrology

Artifacts often limit the application of computed tomography (CT) in industrial metrology. In order to correct these artifacts, the so-called simulation-based artifact correction (SBAC) was developed in this thesis. For this purpose, analytical and Monte Carlo (MC) based models were set up to simulate the CT measurement process for a given component as accurately and efficiently as possible. Calculating the difference between this simulation and an ideal one yields an estimate of the present artifacts that can be used to correct the corresponding CT measurement. The potential of this approach was demonstrated for the correction of the most common CT artifacts, i.e. beam hardening, x-ray scattering, off-focal radiation, partial volume effects, and cone-beam artifacts. In any case, the SBAC provided CT reconstructions that showed almost no artifacts and whose quality was clearly superior to state-of-the-art reference approaches. In this context, the problem of long runtimes of scatter simulations was solved by another novel approach, the so-called deep scatter estimation (DSE). The DSE uses a deep convolutional neural network which was trained to map the acquired projection data to given MC scatter estimates. Once the DSE network is trained, it can be used to process unknown data in real-time. In different simulation studies and measurements, it could be shown that DSE generalizes to various acquisition conditions and components while providing scatter distributions that differ by less than 2 % from MC simulations. Thus, the two developed approaches make an important contribution to correct CT artifacts efficiently and to extend the applicability of CT in the field of industrial metrology

Conta cellulare su immagini di endotelio corneale mediante utilizzo di reti neurali

Il presente elaborato tratterà algoritmi in grado di stimare, in modo automatico e per mezzo di regressione la quantità di cellule presenti nell'endotelio corneale a partire da immagini acquisite in vivo grazie alle tecniche di microscopia confocale o speculare. L' approccio utilizzato si avvale dell'utilizzo di oggetti puntiformi per individuare la posizione delle cellule