BDFormer: Boundary-aware dual-decoder transformer for skin lesion segmentation.

Segmenting skin lesions from dermatoscopic images is crucial for improving the quantitative analysis of skin cancer. However, automatic segmentation of skin lesions remains a challenging task due to the presence of unclear boundaries, artifacts, and obstacles such as hair and veins, all of which complicate the segmentation process. Transformers have demonstrated superior capabilities in capturing long-range dependencies through self-attention mechanisms and are gradually replacing CNNs in this domain.

Sparse Coding Inspired LSTM and Self-Attention Integration for Medical Image Segmentation.

Accurate and automatic segmentation of medical images plays an essential role in clinical diagnosis and analysis. It has been established that integrating contextual relationships substantially enhances the representational ability of neural networks. Conventionally, Long Short-Term Memory (LSTM) and Self-Attention (SA) mechanisms have been recognized for their proficiency in capturing global dependencies within data.

Controllable editing via diffusion inversion on ultra-widefield fluorescein angiography for the comprehensive analysis of diabetic retinopathy.

By incorporating multiple indicators that facilitate clinical decision making and effective management of diabetic retinopathy (DR), a comprehensive understanding of the progression of the disease can be achieved. However, the diversity of DR complications poses challenges to the automatic analysis of various information within images. This study aims to establish a deep learning system designed to examine various metrics linked to DR in ultra-widefield fluorescein angiography (UWFA) images.

Corrections to "Image Projection Network: 3D to 2D Image Segmentation in OCTA Images".

In the above article [1], there is an error in (3). Instead of [Formula: see text] It should be [Formula: see text].

Immune checkpoint inhibitor-related chronic pneumonitis: a case report and literature review.

Immune checkpoint inhibitor (ICI)-related chronic pneumonitis is rare. Limited information is available on the characteristics of this condition. Herein, we present the case of a 54-year-old man with recurrent severe ICI-related pneumonitis.

LAGAN: Lesion-Aware Generative Adversarial Networks for Edema Area Segmentation in SD-OCT Images.

Large volume of labeled data is a cornerstone for deep learning (DL) based segmentation methods. Medical images require domain experts to annotate, and full segmentation annotations of large volumes of medical data are difficult, if not impossible, to acquire in practice. Compared with full annotations, image-level labels are multiple orders of magnitude faster and easier to obtain.

Viral Metagenomics Reveals Diverse Viruses in Tissue Samples of Diseased Pigs.

The swine industry plays an essential role in agricultural production in China. Diseases, especially viral diseases, affect the development of the pig industry and threaten human health. However, at present, the tissue virome of diseased pigs has rarely been studied.

LamNet: A Lesion Attention Maps-Guided Network for the Prediction of Choroidal Neovascularization Volume in SD-OCT Images.

Choroidal neovascularization (CNV) volume prediction has an important clinical significance to predict the therapeutic effect and schedule the follow-up. In this paper, we propose a Lesion Attention Maps-Guided Network (LamNet) to automatically predict the CNV volume of next follow-up visit after therapy based on 3-dimentional spectral-domain optical coherence tomography (SD-OCT) images. In particular, the backbone of LamNet is a 3D convolutional neural network (3D-CNN).

Automated Grading of Diabetic Retinopathy with Ultra-Widefield Fluorescein Angiography and Deep Learning.

Purpose: The objective of this study was to establish diagnostic technology to automatically grade the severity of diabetic retinopathy (DR) according to the ischemic index and leakage index with ultra-widefield fluorescein angiography (UWFA) and the Early Treatment Diabetic Retinopathy Study (ETDRS) 7-standard field (7-SF).

Methods: This is a cross-sectional study. UWFA samples from 280 diabetic patients and 119 normal patients were used to train and test an artificial intelligence model to differentiate PDR and NPDR based on the ischemic index and leakage index with UWFA.

Effect of different levels of stroke volume variation on the endothelial glycocalyx of patients undergoing colorectal surgery: A randomized clinical trial.

New Findings: What is the central question of this study? Massive infusion can destroy the endothelial glycocalyx. We compared the serum concentrations of endothelial glycocalyx components and atrial natriuretic peptide and the outcomes of patients with different levels of stroke volume variation (SVV). What is the main finding and its importance? With a decrease in SVV, the serum concentrations of endothelial glycocalyx components and atrial natriuretic peptide increased, whereas the oxygenation index decreased.

Joint Optimization of CycleGAN and CNN Classifier for Detection and Localization of Retinal Pathologies on Color Fundus Photographs.

Retinal related diseases are the leading cause of vision loss, and severe retinal lesion causes irreversible damage to vision. Therefore, the automatic methods for retinal diseases detection based on medical images is essential for timely treatment. Considering that manual diagnosis and analysis of medical images require a large number of qualified experts, deep learning can effectively diagnosis and locate critical biomarkers.

MS-CAM: Multi-Scale Class Activation Maps for Weakly-Supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images.

As one of the most critical characteristics in advanced stage of non-exudative Age-related Macular Degeneration (AMD), Geographic Atrophy (GA) is one of the significant causes of sustained visual acuity loss. Automatic localization of retinal regions affected by GA is a fundamental step for clinical diagnosis. In this paper, we present a novel weakly supervised model for GA segmentation in Spectral-Domain Optical Coherence Tomography (SD-OCT) images.

Image Projection Network: 3D to 2D Image Segmentation in OCTA Images.

We present an image projection network (IPN), which is a novel end-to-end architecture and can achieve 3D-to-2D image segmentation in optical coherence tomography angiography (OCTA) images. Our key insight is to build a projection learning module (PLM) which uses a unidirectional pooling layer to conduct effective features selection and dimension reduction concurrently. By combining multiple PLMs, the proposed network can input 3D OCTA data, and output 2D segmentation results such as retinal vessel segmentation.

Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging.

Background And Objective: Accurate assessment of geographic atrophy (GA) is critical for diagnosis and therapy of non-exudative age-related macular degeneration (AMD). Herein, we propose a novel GA segmentation framework for spectral-domain optical coherence tomography (SD-OCT) images that employs synthesized fundus autofluorescence (FAF) images.

Methods: An en-face OCT image is created via the restricted sub-volume projection of three-dimensional OCT data.

Automated Quantification of Hyperreflective Foci in SD-OCT With Diabetic Retinopathy.

The presence of hyperreflective foci (HFs) is related to retinal disease progression, and the quantity has proven to be a prognostic factor of visual and anatomical outcome in various retinal diseases. However, lack of efficient quantitative tools for evaluating the HFs has deprived ophthalmologist of assessing the volume of HFs. For this reason, we propose an automated quantification algorithm to segment and quantify HFs in spectral domain optical coherence tomography (SD-OCT).

Hyper-reflective foci segmentation in SD-OCT retinal images with diabetic retinopathy using deep convolutional neural networks.

Purpose: The purpose of this study was to automatically and accurately segment hyper-reflective foci (HRF) in spectral domain optical coherence tomography (SD-OCT) images with diabetic retinopathy (DR) using deep convolutional neural networks.

Methods: An automatic HRF segmentation model for SD-OCT images based on deep networks was constructed. The model segmented small lesions through pixel-wise predictions based on small image patches.

Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images.

Background And Objective: Quantitative assessment of subretinal fluid in spectral domain optical coherence tomography (SD-OCT) images is crucial for the diagnosis of central serous chorioretinopathy. For the subretinal fluid segmentation, the traditional methods need to segment retinal layers and then segment subretinal fluid. The layer segmentation has a high influence on subretinal fluid segmentation, so we aim to develop a deep learning model to segment subretinal fluid automatically without layer segmentation.

RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.

Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been proposed.

Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning model.

Automatic and reliable segmentation for geographic atrophy in spectral-domain optical coherence tomography (SD-OCT) images is a challenging task. To develop an effective segmentation method, a two-stage deep learning framework based on an auto-encoder is proposed. Firstly, the axial data of cross-section images were used as samples instead of the projection images of SD-OCT images.

Probabilistic Diffusion for Interactive Image Segmentation.

This paper presents an interactive image segmentation approach in which we formulate segmentation as a probabilistic estimation problem based on the prior user intention. Instead of directly measuring the relationship between pixels and labels, we first estimate the distances between pixel pairs and label pairs using a probabilistic framework. Then, binary probabilities with label pairs are naturally converted to unary probabilities with labels.

Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images.

Purpose: To automatically and accurately segment geographic atrophy (GA) in spectral-domain optical coherence tomography (SD-OCT) images by constructing a voting system with deep neural networks without the use of retinal layer segmentation.

Methods: An automatic GA segmentation method for SD-OCT images based on the deep network was constructed. The structure of the deep network was composed of five layers, including one input layer, three hidden layers, and one output layer.