Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Medscape

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

IEEE Transactions on Pattern Analysis and Machine Intelligence

X-ray computer tomography (CT) is fast becoming an accepted tool within the materials science community for the acquisition of 3D images. Here the authors review the current state of the art as CT transforms from a qualitative diagnostic tool to a quantitative one. Our review considers first the image acquisition process, including the use of iterative reconstruction strategies suited to specific segmentation tasks and emerging methods that provide more insight (e.g. fast and high resolution imaging, crystallite (grain) imaging) than conventional attenuation based tomography. Methods and shortcomings of CT are examined for the quantification of 3D volumetric data to extract key topological parameters such as phase fractions, phase contiguity, and damage levels as well as density variations. As a non-destructive technique, CT is an ideal means of following structural development over time via time lapse sequences of 3D images (sometimes called 3D movies or 4D imaging). This includes information nee...

https://www.tandfonline.com/doi/pdf/10.1179/1743280413Y.0000000023

Quantitative X-ray tomography

In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods.

Low-dose CT via convolutional neural network

A segmentation method for dissected aorta in a CT image based on a convolutional neural network. The method comprises: combining a CT dissected aorta segmentation algorithm for a three-dimensional convolutional neural network and a two-dimensional convolutional neural network, dividing three-dimensional volume data into two parts by using the three-dimensional convolutional neural network, and then segmenting the two parts by using two two-dimensional convolutional neural networks respectively to obtain a final segmentation result. The method can effectively segment the dissected aorta from a CT image containing the dissected aorta, thereby overcoming the shortcomings of poor segmentation precision caused by the contradiction between the input image resolution and the GPU memory capacity because only a three-dimensional full convolutional neural network is used conventionally, and the instability of a segmentation effect caused by the loss of three-dimensional information when only the two-dimensional convolutional neural network is used, and thus presenting a good segmentation effect.

Segmentation method for dissected aorta in ct image based on convolutional neural network

Accurate segmentation of topological tubular structures, such as blood vessels and roads, is crucial in various fields, ensuring accuracy and efficiency in downstream tasks. However, many factors complicate the task, including thin local structures and variable global morphologies. In this work, we note the specificity of tubular structures and use this knowledge to guide our DSCNet to simultaneously enhance perception in three stages: feature extraction, feature fusion, and loss constraint. First, we propose a dynamic snake convolution to accurately capture the features of tubular structures by adaptively focusing on slender and tortuous local structures. Subsequently, we propose a multi-view feature fusion strategy to complement the attention to features from multiple perspectives during feature fusion, ensuring the retention of important information from different global morphologies. Finally, a continuity constraint loss function, based on persistent homology, is proposed to constrain the topological continuity of the segmentation better. Experiments on 2D and 3D datasets show that our DSCNet provides better accuracy and continuity on the tubular structure segmentation task compared with several methods. Our codes will be publicly available.

Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation

In this paper, we propose a novel Single Noisy Audio De-noising Framework (SNA-DF) for speech denoising using only single noisy audio samples, which overcomes the limi-tation of constructing either noisy-clean training pairs or multiple independent noisy audio samples. The proposed SNA-DF contains two modules: training audio pairs gener-ated module and audio denoising module. The first module adopts a random audio sub-sampler on single noisy audio samples for the generation of training audio pairs. The sub-sampled training audio pairs are then fed into the audio denoising module, which employs a deep complex U-Net incorporating a complex two-stage transformer (cTSTM) to extract both magnitude and phase information for taking full advantage of the complex features of single noisy au-dios. Experimental results show that the proposed SNA-DF not only eliminates the high dependence on clean targets of traditional audio denoising methods, but also outperforms the methods using multiple noisy audio samples.

Speech Denoising Using Only Single Noisy Audio Samples.

Purpose Nephron-sparing surgery (NSS) is a mainstream treatment for localized renal tumors. Segmental renal artery clamping (SRAC) is commonly used in NSS. Automatic and precise segmentations of renal artery trees are required to improve the workflow of SRAC in NSS. In this study, we developed a tridimensional kidney perfusion (TKP) model based on deep learning technique to automatically demonstrate renal artery segmentation, and verified the precision and feasibility during laparoscopic partial nephrectomy (PN). Methods The TKP model was established based on convolutional neural network (CNN), and the precision was validated in porcine models. From April 2018 to January 2020, TKP model was applied in laparoscopic PN in 131 patients with T1a tumors. Demographics, perioperative variables, and data from the TKP models were assessed. Indocyanine green (ICG) with near-infrared fluorescence (NIRF) imaging was applied after clamping and dice coefficient was used to evaluate the precision of the model. Results The precision of the TKP model was validated in porcine models with the mean dice coefficient of 0.82. Laparoscopic PN was successfully performed in all cases with segmental renal artery clamping (SRAC) under TKP model’s guidance. The mean operation time was 100.8 min; the median estimated blood loss was 110 ml. The ischemic regions recorded in NIRF imaging were highly consistent with the perfusion regions in the TKP models (mean dice coefficient = 0.81). Multivariate analysis revealed that the feeding lobar artery number was strongly correlated with tumor size and contact surface area; the supplying segmental arteries number correlated with tumor size. Conclusions Using the CNN technique, the TKP model is developed to automatically present the renal artery trees and precisely delineate the perfusion regions of different segmental arteries. The guidance of the TKP model is feasible and effective in nephron-sparing surgery.

/pdf/a-novel-3d-deep-learning-model-to-automatically-demonstrate-s0fo8bly.pdf

A novel 3D deep learning model to automatically demonstrate renal artery segmentation and its validation in nephron-sparing surgery

Pulmonary arterial hypertension (PAH) prognosis prediction on 3D non-contrast CT images is one of the most important tasks for PAH treatment. It will help clinicians stratify patients into different groups for early diagnosis and timely intervention via automatically extracting the potential biomarkers of PAH to predict mortality. However, it is still a task of great challenges due to the large volume and low-contrast regions of interest in 3D chest CT images. In this paper, we propose the first multi-task learning-based PAH prognosis prediction framework, <italic>P<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-Net</italic>, which effectively optimizes the model and powerfully represents task-dependent features via our <italic>Memory Drift (MD)</italic> and <italic>Prior Prompt Learning (PPL)</italic> strategies. <bold>1)</bold> Our MD maintains a large memory bank to provide a dense sampling of the deep biomarkers' distribution. Therefore, although the batch size is very small caused by our large volume, a reliable (negative log partial) likelihood loss is still able to be calculated on a representative probability distribution for robust optimization. <bold>2)</bold> Our PPL simultaneously learns an additional manual biomarkers prediction task to embed clinical prior knowledge into our deep prognosis prediction task in hidden and explicit ways. Therefore, it will prompt the prediction of deep biomarkers and improve the perception of task-dependent features in our low-contrast regions. Our P<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-Net achieves a high prognostic correlation of the prediction and great generalization with the highest 70.19% C-index and 2.14 HR. Extensive experiments with promising results on our PAH prognosis prediction reveal powerful prognosis performance and great clinical significance in PAH treatment. All of our code will be made publicly available online.

Guanyu Yang

Papers

Segmentation method for dissected aorta in ct image based on convolutional neural network

Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation

Speech Denoising Using Only Single Noisy Audio Samples.

A novel 3D deep learning model to automatically demonstrate renal artery segmentation and its validation in nephron-sparing surgery

Multi-Task Learning for Pulmonary Arterial Hypertension Prognosis Prediction via Memory Drift and Prior Prompt Learning on 3D Chest CT