Developing new methods for the automated analysis of clinical fetal and neonatal MRI data is limited by the scarcity of annotated pathological datasets and privacy concerns that often restrict data sharing, hindering the effectiveness of deep learning models. We address this in two ways. First, we introduce Fetal&Neonatal-DDPM, a novel diffusion model framework designed to generate high-quality synthetic pathological fetal and neonatal MRIs from semantic label images. Second, we enhance training data by modifying healthy label images through morphological alterations to simulate conditions such as ventriculomegaly, cerebellar and pontocerebellar hypoplasia, and microcephaly. By leveraging Fetal&Neonatal-DDPM, we synthesize realistic pathological MRIs from these modified pathological label images. Radiologists rated the synthetic MRIs as significantly (p < 0.05) superior in quality and diagnostic value compared to real MRIs, demonstrating features such as blood vessels and choroid plexus, and improved alignment with label annotations. Synthetic pathological data enhanced state-of-the-art nnUNet segmentation performance, particularly for severe ventriculomegaly cases, with the greatest improvements achieved in ventricle segmentation (Dice scores: 0.9253 vs. 0.7317). This study underscores the potential of generative AI as transformative tool for data augmentation, offering improved segmentation performance in pathological cases. This development represents a significant step towards improving analysis and segmentation accuracy in prenatal imaging, and also offers new ways for data anonymization through the generation of pathologic image data.

Inpainting involves filling in missing pixels or areas in an image, a crucial technique employed in Mixed Reality environments for various applications, particularly in Diminished Reality (DR) where content is removed from a user's visual environment. Existing methods rely on digital replacement techniques which necessitate multiple cameras and incur high costs. AR devices and smartphones use ToF depth sensors to capture scene depth maps aligned with RGB images. Despite speed and affordability, ToF cameras create imperfect depth maps with missing pixels. To address the above challenges, we propose Hierarchical Inpainting GAN (HI-GAN), a novel approach comprising three GANs in a hierarchical fashion for RGBD inpainting. EdgeGAN and LabelGAN inpaint masked edge and segmentation label images respectively, while CombinedRGBD-GAN combines their latent representation outputs and performs RGB and Depth inpainting. Edge images and particularly segmentation label images as auxiliary inputs significantly enhance inpainting performance by complementary context and hierarchical optimization. We believe we make the first attempt to incorporate label images into inpainting process.Unlike previous approaches requiring multiple sequential models and separate outputs, our work operates in an end-to-end manner, training all three models simultaneously and hierarchically. Specifically, EdgeGAN and LabelGAN are first optimized separately and further optimized inside CombinedRGBD-GAN to enhance inpainting quality. Experiments demonstrate that HI-GAN works seamlessly and achieves overall superior performance compared with existing approaches.

Motion artifact reduction is one of the important research topics in MR imaging, as the motion artifact degrades image quality and makes diagnosis difficult. Recently, many deep learning approaches have been studied for motion artifact reduction. Unfortunately, most existing models are trained in a supervised manner, requiring paired motion-corrupted and motion-free images, or are based on a strict motion-corruption model, which limits their use for real-world situations. To address this issue, here we present an annealed score-based diffusion model for MRI motion artifact reduction. Specifically, we train a score-based model using only motion-free images, and then motion artifacts are removed by applying forward and reverse diffusion processes repeatedly to gradually impose a low-frequency data consistency. Experimental results verify that the proposed method successfully reduces both simulated and in vivo motion artifacts, outperforming the state-of-the-art deep learning methods.

Cars are central to the cinema in a variety of ways. While the railroad and trains were prominent during the silent era -- and in the westerns that continued to be produced well into the 1970s -- automobiles offer greater freedom of movement than trains do and thus offer greater cinematic possibilities. So extensive is this relationship that the car chase has almost become a mini-genre unto itself. Yet film scholars have not yet dedicated any work to exploring this subject in depth. But we can start by examining the relationship between cinema and transportation more broadly.

Are you tired to label your images by hand to work with object detection? Have hundreds or thousands of images to label? Then this project will make your life easier, just create some annotations and let the machine do the rest for you! If you have trouble or doubt check my tutorial on medium. You can also open an issue and I'll hep you!

TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments. Object tracking is an essential image analysis technique used across biosciences to quantify dynamic processes. In life sciences, tracking is used for instance to track single particles, sub-cellular organelles, bacteria, cells, and whole animals.

When he testified before Congress, Facebook CEO Mark ZuckerbergMark Elliot ZuckerbergHillicon Valley: Biden calls on Facebook to change political speech rules Dems demand hearings after Georgia election chaos Microsoft stops selling facial recognition tech to police The Hill's Campaign Report: Biden campaign goes on offensive against Facebook Biden campaign calls on Facebook to change political speech rules MORE loved to tell legislators that his team would "follow up with you" on that, or that his team is building AI tools for that. These AI tools would supposedly solve many content moderation problems, ranging from misinformation to terrorism to fake accounts. Today, you could add coronavirus misinformation to that list, but you could also ask if these AI tools have actually solved any of these problems (or if Zuckerberg's team ever did follow up). Many decisions today, such as ranking a website in search results, are made by algorithms. These algorithms are perceived as objective, mechanical and unbiased, while humans are perceived as subjective, fallible and full of bias.

Data science comes closer to SEO every day. Data science, and more exactly artificial intelligence, isn't new, but it has become trendy in our industry over the past few years. Data science crosses paths with both big data and artificial intelligence when it comes to analyzing and processing data known as datasets. Google Trends does a pretty good job of illustrating that data science, as a subject of intent, has been increasing over the years since 2004. The user intent for "machine learning" has been increasing as well, and is one of the most popular search queries.

Since our task is detection and not segmentation, correctly predicting only a sufficient amount of voxels around the vertebra centroid is needed to detect normal or fractured vertebrae in an image. We leverage this observation to construct 3D label images for our training database in a semi-automated fashion. First, radiologist S.R. created a text file with annotations for every vertebra present in the field of view as described in section 2. Next, J.N. enriched these labels with 3D centroid coordinates by manually localizing every vertebra centroid in the image using MeVisLab [8]. This step required an average of less than two minutes per image in our dataset. Finally, we extended the method described by Glocker et al. [6] to automatically generate 3D label images from these sparse annotations. The resulting label images contain ellipsoids (flattened along the longitudinal axis for fractured vertebrae) around each vertebra centroid annotated with the ground truth class label provided by the radiologist (combining mild, moderate and severe fractures into one fracture class because of the low number of examples per class, see Figure 1).

Detecting a specific horizon in seismic images is a valuable tool for geological interpretation. Because hand-picking the locations of the horizon is a time-consuming process, automated computational methods were developed starting three decades ago. Older techniques for such picking include interpolation of control points however, in recent years neural networks have been used for this task. Until now, most networks trained on small patches from larger images. This limits the networks ability to learn from large-scale geologic structures. Moreover, currently available networks and training strategies require label patches that have full and continuous annotations, which are also time-consuming to generate. We propose a projected loss-function for training convolutional networks with a multi-resolution structure, including variants of the U-net. Our networks learn from a small number of large seismic images without creating patches. The projected loss-function enables training on labels with just a few annotated pixels and has no issue with the other unknown label pixels. Training uses all data without reserving some for validation. Only the labels are split into training/testing. Contrary to other work on horizon tracking, we train the network to perform non-linear regression, and not classification. As such, we propose labels as the convolution of a Gaussian kernel and the known horizon locations that indicate uncertainty in the labels. The network output is the probability of the horizon location. We demonstrate the proposed computational ingredients on two different datasets, for horizon extrapolation and interpolation. We show that the predictions of our methodology are accurate even in areas far from known horizon locations because our learning strategy exploits all data in large seismic images.