Automatic detection of pathological regions in medical images

Medical images are an essential tool in the daily clinical routine for the detection, diagnosis, and monitoring of diseases. Different imaging modalities such as magnetic resonance (MR) or X-ray imaging are used to visualize the manifestations of various diseases, providing physicians with valuable information. However, analyzing every single image by human experts is a tedious and laborious task. Deep learning methods have shown great potential to support this process, but many images are needed to train reliable neural networks. Besides the accuracy of the final method, the interpretability of the results is crucial for a deep learning method to be established. A fundamental problem in the medical field is the availability of sufficiently large datasets due to the variability of different imaging techniques and their configurations. The aim of this thesis is the development of deep learning methods for the automatic identification of anomalous regions in medical images. Each method is tailored to the amount and type of available data. In the first step, we present a fully supervised segmentation method based on denoising diffusion models. This requires a large dataset with pixel-wise manual annotations of the pathological regions. Due to the implicit ensemble characteristic, our method provides uncertainty maps to allow interpretability of the model’s decisions. Manual pixel-wise annotations face the problems that they are prone to human bias, hard to obtain, and often even unavailable. Weakly supervised methods avoid these issues by only relying on image-level annotations. We present two different approaches based on generative models to generate pixel-wise anomaly maps using only image-level annotations, i.e., a generative adversarial network and a denoising diffusion model. Both perform image-to-image translation between a set of healthy and a set of diseased subjects. Pixel-wise anomaly maps can be obtained by computing the difference between the original image of the diseased subject and the synthetic image of its healthy representation. In an extension of the diffusion-based anomaly detection method, we present a flexible framework to solve various image-to-image translation tasks. With this method, we managed to change the size of tumors in MR images, and we were able to add realistic pathologies to images of healthy subjects. Finally, we focus on a problem frequently occurring when working with MR images: If not enough data from one MR scanner are available, data from other scanners need to be considered. This multi-scanner setting introduces a bias between the datasets of different scanners, limiting the performance of deep learning models. We present a regularization strategy on the model’s latent space to overcome the problems raised by this multi-site setting

Diffusion Models for Memory-efficient Processing of 3D Medical Images

Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources. This limits their application to medical tasks, where we often deal with large 3D volumes, like high-resolution three-dimensional data. In this work, we present a number of different ways to reduce the resource consumption for 3D diffusion models and apply them to a dataset of 3D images. The main contribution of this paper is the memory-efficient patch-based diffusion model \textit{PatchDDM}, which can be applied to the total volume during inference while the training is performed only on patches. While the proposed diffusion model can be applied to any image generation tasks, we evaluate the method on the tumor segmentation task of the BraTS2020 dataset and demonstrate that we can generate meaningful three-dimensional segmentations.Comment: Accepted at MIDL 202

Point Cloud Diffusion Models for Automatic Implant Generation

Advances in 3D printing of biocompatible materials make patient-specific implants increasingly popular. The design of these implants is, however, still a tedious and largely manual process. Existing approaches to automate implant generation are mainly based on 3D U-Net architectures on downsampled or patch-wise data, which can result in a loss of detail or contextual information. Following the recent success of Diffusion Probabilistic Models, we propose a novel approach for implant generation based on a combination of 3D point cloud diffusion models and voxelization networks. Due to the stochastic sampling process in our diffusion model, we can propose an ensemble of different implants per defect, from which the physicians can choose the most suitable one. We evaluate our method on the SkullBreak and SkullFix datasets, generating high-quality implants and achieving competitive evaluation scores

Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis

The limited availability of large image datasets, mainly due to data privacy and differences in acquisition protocols or hardware, is a significant issue in the development of accurate and generalizable machine learning methods in medicine. This is especially the case for Magnetic Resonance (MR) images, where different MR scanners introduce a bias that limits the performance of a machine learning model. We present a novel method that learns to ignore the scanner-related features present in MR images, by introducing specific additional constraints on the latent space. We focus on a real-world classification scenario, where only a small dataset provides images of all classes. Our method \textit{Learn to Ignore (L2I)} outperforms state-of-the-art domain adaptation methods on a multi-site MR dataset for a classification task between multiple sclerosis patients and healthy controls

Generative AI for Medical Imaging: extending the MONAI Framework

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features

Diffusion Models for Medical Anomaly Detection

In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training. Current anomaly detection methods mainly rely on generative adversarial networks or autoencoder models. Those models are often complicated to train or have difficulties to preserve fine details in the image. We present a novel weakly supervised anomaly detection method based on denoising diffusion implicit models. We combine the deterministic iterative noising and denoising scheme with classifier guidance for image-to-image translation between diseased and healthy subjects. Our method generates very detailed anomaly maps without the need for a complex training procedure. We evaluate our method on the BRATS2020 dataset for brain tumor detection and the CheXpert dataset for detecting pleural effusions