Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets

Multi-organ segmentation has extensive applications in many clinical applications. To segment multiple organs of interest, it is generally quite difficult to collect full annotations of all the organs on the same images, as some medical centers might only annotate a portion of the organs due to their own clinical practice. In most scenarios, one might obtain annotations of a single or a few organs from one training set, and obtain annotations of the the other organs from another set of training images. Existing approaches mostly train and deploy a single model for each subset of organs, which are memory intensive and also time inefficient. In this paper, we propose to co-train weight-averaged models for learning a unified multi-organ segmentation network from few-organ datasets. We collaboratively train two networks and let the coupled networks teach each other on un-annotated organs. To alleviate the noisy teaching supervisions between the networks, the weighted-averaged models are adopted to produce more reliable soft labels. In addition, a novel region mask is utilized to selectively apply the consistent constraint on the un-annotated organ regions that require collaborative teaching, which further boosts the performance. Extensive experiments on three public available single-organ datasets LiTS, KiTS, Pancreas and manually-constructed single-organ datasets from MOBA show that our method can better utilize the few-organ datasets and achieves superior performance with less inference computational cost.Comment: Accepted by MICCAI 202

Cirrhosis Classification Based on Texture Classification of Random Features

Accurate staging of hepatic cirrhosis is important in investigating the cause and slowing down the effects of cirrhosis. Computer-aided diagnosis (CAD) can provide doctors with an alternative second opinion and assist them to make a specific treatment with accurate cirrhosis stage. MRI has many advantages, including high resolution for soft tissue, no radiation, and multiparameters imaging modalities. So in this paper, multisequences MRIs, including T1-weighted, T2-weighted, arterial, portal venous, and equilibrium phase, are applied. However, CAD does not meet the clinical needs of cirrhosis and few researchers are concerned with it at present. Cirrhosis is characterized by the presence of widespread fibrosis and regenerative nodules in the hepatic, leading to different texture patterns of different stages. So, extracting texture feature is the primary task. Compared with typical gray level cooccurrence matrix (GLCM) features, texture classification from random features provides an effective way, and we adopt it and propose CCTCRF for triple classification (normal, early, and middle and advanced stage). CCTCRF does not need strong assumptions except the sparse character of image, contains sufficient texture information, includes concise and effective process, and makes case decision with high accuracy. Experimental results also illustrate the satisfying performance and they are also compared with typical NN with GLCM

Deep Learning for Logo Detection: A Survey

When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, etc. have been employed. This paper reviews the advance in applying deep learning techniques to logo detection. Firstly, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and the strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection to complete this survey

An overview of Antarctic polynyas: sea ice production, forcing mechanisms, temporal variability and water mass formation

Polynyas are irregular open water bodies within the sea ice cover in polar regions under freezing weather conditions. In this study, we reviewed the progress of research work on dynamical forcing, sea ice production (SIP), and water mass formation for both coastal polynyas and open-ocean polynyas in the Southern Ocean, as well as the variability and controlling mechanisms of polynya processes on different time scales. Polynyas play an irreplaceable role in the regulation of global ocean circulation and biological processes in regional ocean ecosystems. The coastal polynyas (latent heat polynyas) are mainly located in the Weddell Sea, the Ross Sea and on the west side of protruding topographic features in East Antarctica. During the formation of coastal polynyas, which are mainly forced by offshore winds or ocean currents, brine rejection triggered by high SIP results in the formation of high salinity shelf water, which is the predecessor of the Antarctic bottom water — the lower limb of the global thermohaline circulation. The open-ocean polynyas (sensible heat polynyas) are mainly found in the Indian sector of the Southern Ocean, which are formed by ocean convection processes generated by topography and negative wind stress curl. The convection processes bring nutrients into the upper ocean, which supports biological production and makes the polynya regions an important sink for atmospheric carbon dioxide. The limitations and challenges in polynya research are also discussed

Integrated microfluidic systems with sample preparation and nucleic acid amplification

Rapid, efficient and accurate nucleic acid molecule detection is important in the screening of diseases and pathogens, yet remains a limiting factor at point of care (POC) treatment. Microfluidic systems are characterized by fast, integrated, miniaturized features which provide an effective platform for qualitative and quantitative detection of nucleic acid molecules. The nucleic acid detection process mainly includes sample preparation and target molecule amplification. Given the advancements in theoretical research and technological innovations to date, nucleic acid extraction and amplification integrated with microfluidic systems has advanced rapidly. The primary goal of this review is to outline current approaches used for nucleic acid detection in the context of microfluidic systems. The secondary goal is to identify new approaches that will help shape future trends at the intersection of nucleic acid detection and microfluidics, particularly with regard to increasing disease and pathogen detection for improved diagnosis and treatment

TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency

Accurate segmentation of brain tumors from medical images is important for diagnosis and treatment planning, and it often requires multi-modal or contrast-enhanced images. However, in practice some modalities of a patient may be absent. Synthesizing the missing modality has a potential for filling this gap and achieving high segmentation performance. Existing methods often treat the synthesis and segmentation tasks separately or consider them jointly but without effective regularization of the complex joint model, leading to limited performance. We propose a novel brain Tumor Image Synthesis and Segmentation network (TISS-Net) that obtains the synthesized target modality and segmentation of brain tumors end-to-end with high performance. First, we propose a dual-task-regularized generator that simultaneously obtains a synthesized target modality and a coarse segmentation, which leverages a tumor-aware synthesis loss with perceptibility regularization to minimize the high-level semantic domain gap between synthesized and real target modalities. Based on the synthesized image and the coarse segmentation, we further propose a dual-task segmentor that predicts a refined segmentation and error in the coarse segmentation simultaneously, where a consistency between these two predictions is introduced for regularization. Our TISS-Net was validated with two applications: synthesizing FLAIR images for whole glioma segmentation, and synthesizing contrast-enhanced T1 images for Vestibular Schwannoma segmentation. Experimental results showed that our TISS-Net largely improved the segmentation accuracy compared with direct segmentation from the available modalities, and it outperformed state-of-the-art image synthesis-based segmentation methods

STEP: Spatiotemporal enhancement pattern for MR-based breast tumor diagnosis: MR-based breast tumor diagnosis

The authors propose a spatiotemporal enhancement pattern (STEP) for comprehensive characterization of breast tumors in contrast-enhanced MR images. By viewing serial contrast-enhanced MR images as a single spatiotemporal image, they formulate the STEP as a combination of (1) dynamic enhancement and architectural features of a tumor, and (2) the spatial variations of pixelwise temporal enhancements. Although the latter has been widely used by radiologists for diagnostic purposes, it has rarely been employed for computer-aided diagnosis. This article presents two major contributions. First, the STEP features are introduced to capture temporal enhancement and its spatial variations. This is essentially carried out through the Fourier transformation and pharmacokinetic modeling of various temporal enhancement features, followed by the calculation of moment invariants and Gabor texture features. Second, for effectively extracting the STEP features from tumors, we develop a graph-cut based segmentation algorithm that aims at refining coarse manual segmentations of tumors. The STEP features are assessed through their diagnostic performance for differentiating between benign and malignant tumors using a linear classifier (along with a simple ranking-based feature selection) in a leave-one-out cross-validation setting. The experimental results for the proposed features exhibit superior performance, when compared to the existing approaches, with the area under the ROC curve approaching 0.97