Pretraining foundation models for chest X-rays have improved by leveraging paired radiographs and radiology reports, sometimes augmented with medical definitions. However, current approaches often fail to encode rich, granular medical knowledge: radiology reports capture current disease manifestations, while abstract definitions remain overly generalized. To address this, DeViDe integrates open-source radiographic disease descriptions from the web (detailing general visual characteristics of diseases) alongside radiology reports and abstract definitions to form a comprehensive knowledge snapshot. DeViDe introduces three key novelties: (i) web-derived radiographic descriptions and their augmentation using large language models; (ii) a pipeline to enrich raw radiology reports with knowledge from these descriptions; and (iii) a multi-label alignment strategy to effectively align each image with multiple descriptions. Extensive experiments show that in zero-shot settings, DeViDe performs competitively with fully supervised models on external datasets and achieves state-of-the-art results on four large-scale benchmarks. Furthermore, fine-tuning on five classification and three segmentation tasks confirms its superior performance across diverse data distributions.

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here, we present a novel strategy based on residual learning for retrospective motion correction in fast 3D whole-brain multiparametric MRI. We propose a 3D multiscale convolutional neural network (CNN) that learns the non-linear relationship between the motion-affected quantitative parameter maps and the residual error to their motion-free reference. For supervised model training, despite limited data availability, we propose a physics-informed simulation to generate self-contained paired datasets from a priori motion-free data. We evaluate motion-correction performance of the proposed method for the example of 3D Quantitative Transient-state Imaging at 1.5T and 3T. We show the robustness of the motion correction for various motion regimes and demonstrate the generalization capabilities of the residual CNN in terms of real-motion in vivo data of healthy volunteers and clinical patient cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that the proposed motion correction outperforms current state of the art, reliably providing a high, clinically relevant image quality for mild to pronounced patient movements. This has important implications in clinical setups where large amounts of motion affected data must be discarded as they are rendered diagnostically unusable.

Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic; particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerlineDice (short clDice), which is calculated on the inter-section of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. Extending this, we pro-pose a computationally efficient, differentiable loss function (soft-clDice) for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores.

Accurate and automatic organ segmentation is critical for computer-aided analysis towards clinical decision support and treatment planning. State-of-the-art approaches have achieved remarkable segmentation accuracy on large organs, such as the liver and kidneys. However, most of these methods do not perform well on small organs, such as the pancreas, gallbladder, and adrenal glands, especially when lacking sufficient training data. This paper presents an automatic approach for small organ segmentation with limited training data using two cascaded steps-localization and segmentation. The localization stage involves the extraction of the region of interest after the registration of images to a common template and during the segmentation stage, a voxel-wise label map of the extracted region of interest is obtained and then transformed back to the original space. In the localization step, we propose to utilize a graph-based groupwise image registration method to build the template for registration so as to minimize the potential bias and avoid getting a fuzzy template. More importantly, a novel knowledge-aided convolutional neural network is proposed to improve segmentation accuracy in the second stage. This proposed network is flexible and can combine the effort of both deep learning and traditional methods, consequently achieving better segmentation relative to either of individual methods. The ISBI 2015 VISCERAL challenge dataset is used to evaluate the presented approach. Experimental results demonstrate that the proposed method outperforms cutting-edge deep learning approaches, traditional forest-based approaches, and multi-atlas approaches in the segmentation of small organs.