CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images.

The development of successful artificial intelligence models for chest X-ray analysis relies on large, diverse datasets with high-quality annotations. While several databases of chest X-ray images have been released, most include disease diagnosis labels but lack detailed pixel-level anatomical segmentation labels. To address this gap, we introduce an extensive chest X-ray multi-center segmentation dataset with uniform and fine-grain anatomical annotations for images coming from five well-known publicly available databases: ChestX-ray8, CheXpert, MIMIC-CXR-JPG, Padchest, and VinDr-CXR, resulting in 657,566 segmentation masks. Our methodology utilizes the HybridGNet model to ensure consistent and high-quality segmentations across all datasets. Rigorous validation, including expert physician evaluation and automatic quality control, was conducted to validate the resulting masks. Additionally, we provide individualized quality indices per mask and an overall quality estimation per dataset. This dataset serves as a valuable resource for the broader scientific community, streamlining the development and assessment of innovative methodologies in chest X-ray analysis.

The long intergenic noncoding RNA ARES modulates root architecture in Arabidopsis.

Long noncoding RNAs (lncRNAs) have emerged as important regulators of gene expression in plants. They have been linked to a wide range of molecular mechanisms, including epigenetics, miRNA activity, RNA processing and translation, and protein localization or stability. In Arabidopsis, characterized lncRNAs have been implicated in several physiological contexts, including plant development and the response to the environment. Here we searched for lncRNA loci located nearby key genes involved in root development and identified the lncRNA ARES (AUXIN REGULATOR ELEMENT DOWNSTREAM SOLITARYROOT) downstream of the lateral root master gene IAA14/SOLITARYROOT (SLR). Although ARES and IAA14 are co-regulated during development, the knockdown and knockout of ARES did not affect IAA14 expression. However, in response to exogenous auxin, ARES knockdown impairs the induction of its other neighboring gene encoding the transcription factor NF-YB3. Furthermore, knockdown/out of ARES results in a root developmental phenotype in control conditions. Accordingly, a transcriptomic analysis revealed that a subset of ARF7-dependent genes is deregulated. Altogether, our results hint at the lncRNA ARES as a novel regulator of the auxin response governing lateral root development, likely by modulating gene expression in trans.

Improving Anatomical Plausibility in Medical Image Segmentation via Hybrid Graph Neural Networks: Applications to Chest X-Ray Analysis.

Anatomical segmentation is a fundamental task in medical image computing, generally tackled with fully convolutional neural networks which produce dense segmentation masks. These models are often trained with loss functions such as cross-entropy or Dice, which assume pixels to be independent of each other, thus ignoring topological errors and anatomical inconsistencies. We address this limitation by moving from pixel-level to graph representations, which allow to naturally incorporate anatomical constraints by construction. To this end, we introduce HybridGNet, an encoder-decoder neural architecture that leverages standard convolutions for image feature encoding and graph convolutional neural networks (GCNNs) to decode plausible representations of anatomical structures. We also propose a novel image-to-graph skip connection layer which allows localized features to flow from standard convolutional blocks to GCNN blocks, and show that it improves segmentation accuracy. The proposed architecture is extensively evaluated in a variety of domain shift and image occlusion scenarios, and audited considering different types of demographic domain shift. Our comprehensive experimental setup compares HybridGNet with other landmark and pixel-based models for anatomical segmentation in chest x-ray images, and shows that it produces anatomically plausible results in challenging scenarios where other models tend to fail.

ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture.

BACKGROUND: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. RESULTS: We developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. CONCLUSIONS: Our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.