Robust Ellipsoid Fitting Using Axial Distance and Combination

In random sample consensus (RANSAC), the problem of ellipsoid fitting can be formulated as a problem of minimization of point-to-model distance, which is realized by maximizing model score. Hence, the performance of ellipsoid fitting is affected by distance metric. In this paper, we proposed a novel distance metric called the axial distance, which is converted from the algebraic distance by introducing a scaling factor to solve nongeometric problems of the algebraic distance. There is complementarity between the axial distance and Sampson distance because their combination is a stricter metric when calculating the model score of sample consensus and the weight of the weighted least squares (WLS) fitting. Subsequently, a novel sample-consensus-based ellipsoid fitting method is proposed by using the combination between the axial distance and Sampson distance (CAS). We compare the proposed method with several representative fitting methods through experiments on synthetic and real datasets. The results show that the proposed method has a higher robustness against outliers, consistently high accuracy, and a speed close to that of the method based on sample consensus.Comment: 13 page

Analysis of 116 cases of rectal cancer treated by transanal local excision

BACKGROUND: The purpose of this research was to evaluate the therapeutic effects and prognostic factors of transanal local excision (TAE) for rectal cancer. METHODS: We retrospectively analyzed 116 cases that underwent TAE for rectal cancer from 1995 to 2008. A Cox regression analysis was used to analyze prognostic factors. RESULTS: The survival times for the patients were from 14 to 160.5 months (median time, 58.5 months). The 5-year and 10-year overall survival rates were 72% and 53%, respectively. In all 16 cases experienced local recurrence (13.8%). Pathological type, recurrence or metastasis, and depth of infiltration (T stage) were the prognostic factors according to the univariate analysis, and the latter two were independent factors affecting patient prognosis. For patients with T1 stage who underwent adjuvant radiotherapy, there was no local recurrence; for those in T2 stage, the local recurrence rate was 14.6%. In addition, there was no difference between the patients who received radiotherapy and those who did not (T1: P = 0.260, T2: P = 0.262 for survival rate and T1: P = 0.480, T2: P = 0.560 for recurrence). CONCLUSIONS: The result of TAE for rectal cancer is satisfactory for T1 stage tumors, but it is not suitable for T2 stage tumors

Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation

Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors

AAU-Net: an Adaptive Attention U-Net for breast lesions segmentation in ultrasound images

Various deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images. Specifically, we introduce a hybrid adaptive attention module (HAAM), which mainly consists of a channel self-attention block and a spatial self-attention block, to replace the traditional convolution operation. Compared with the conventional convolution operation, the design of the hybrid adaptive attention module can help us capture more features under different receptive fields. Different from existing attention mechanisms, the HAAM module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation. Extensive experiments with several state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets show that our method has better performance on breast lesions segmentation. Furthermore, robustness analysis and external experiments demonstrate that our proposed AAU-net has better generalization performance in the breast lesion segmentation. Moreover, the HAAM module can be flexibly applied to existing network frameworks. The source code is available on https://github.com/CGPxy/AAU-net

Melatonin Protects MCAO-Induced Neuronal Loss via NR2A Mediated Prosurvival Pathways

Stroke is the significant cause of human mortality and sufferings depending upon race and demographic location. Melatonin is a potent antioxidant that exerts protective effects in differential experimental stroke models. Several mechanisms have been previously suggested for the neuroprotective effects of melatonin in ischemic brain injury. The aim of this study is to investigate whether melatonin treatment affects the glutamate N-methyl-D-aspartate (NMDA) and alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor signaling in cerebral cortex and striatum 24 h after permanent middle cerebral artery occlusion (MCAO). Melatonin (5 mg/kg) attenuated ischemia-induced down regulation of NMDA receptor 2 (NR2a), postsynaptic density-95 (PSD95) and increases NR2a/PSD95 complex association, which further activates the pro-survival PI3K/Akt/GSK3β pathway with mitigated collapsin response mediator protein 2 (CRMP2) phosphorylation. Furthermore, melatonin increases the expression of γ-enolase, a neurotrophic factor in ischemic cortex and striatum, and preserve the expression of presynaptic (synaptophysin and SNAP25) and postsynaptic (p-GluR1845) protein. Our study demonstrated a novel neuroprotective mechanism for melatonin in ischemic brain injury which could be a promising neuroprotective agent for the treatment of ischemic stroke

Pathological Comparisons of the Hippocampal Changes in the Transient and Permanent Middle Cerebral Artery Occlusion Rat Models

© Copyright © 2019 Shah, Li, Kury, Zeb, Khatoon, Liu, Yang, Liu, Yao, Khan, Koh, Jiang and Li. Ischemic strokes are categorized by permanent or transient obstruction of blood flow, which impedes delivery of oxygen and essential nutrients to brain. In the last decade, the therapeutic window for tPA has increased from 3 to 5–6 h, and a new technique, involving the mechanical removal of the clot (endovascular thrombectomy) to allow reperfusion of the injured area, is being used more often. This last therapeutic approach can be done until 24 h after stroke onset. Due to this fact, more acute ischemic stroke patients are now being recanalized, and so tMCAO is probably the “best” model to address these patients that have a potential good outcome in terms of survival and functional recovery. However, permanent occlusion patients are also important, not only to increase survival rate but also to improve functional outcomes, although these are more difficult to achieve. So, both models are important, and which target different stroke patients in the clinical scenario. Hippocampus has a vital role in memory and cognition, is prone to ischemic induced neurodegeneration. This study was designed to delineate the molecular, pathological, and neurological changes in rat models of t-MCAO, permanent MCAO (pMCAO), and pMCAO with diabetic conditions in hippocampal tissue. Our results showed that these three models showed distinct discrepancies at numerous pathological process, including key signaling molecules involved in neuronal apoptosis, glutamate induced excitotoxicity, neuroinflammation, oxidative stress, and neurotrophic changes. Our result suggests that the two commonly used MCAO models exhibited tremendous differences in terms of neuronal cell loss, glutamate excitotoxic related signaling, synaptic transmission markers, neuron inflammatory and oxidative stress molecules. These differences may reflect the variations in different models, which may provide valuable information for mechanistic and therapeutic inconsistences as experienced in both preclinical models and clinical trials

Finger Vein Recognition Based on a Personalized Best Bit Map

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition

Rational synthesis of oxide nanostructures and surface modification

The main objective of this dissertation is firstly to realize rational synthesis of oxide nanostructures with effective controls on orientation, dimension, density and predesigned position, and through further surface modification of oxide nanostructures to achieve structural and compositional complexity, or to improve functionalities of oxide nanostructures, which are vital for integrating nanostructures into functional nanodevices.DOCTOR OF PHILOSOPHY (SPMS

Rethinking the unpretentious U-net for medical ultrasound image segmentation

Breast tumor segmentation from ultrasound images is one of the key steps that help us characterize and localize tumor regions. However, variable tumor morphology, blurred boundaries, and similar intensity distributions bring challenges for radiologists to segment breast tumors manually. During clinical diagnosis, there are higher demands on the segmentation accuracy and efficiency of breast ultrasound images, so there is an urgent need for an automated method to improve the segmentation accuracy as a technical tool to assist diagnosis. Inspired by the U-net and its many variations, this paper proposed an unpretentious nested U-net (NU-net) for accurate and efficient breast tumor segmentation. The key idea is to utilize U-nets with different depths and shared weights to achieve robust characterization of breast tumors. Specifically, we first utilize the deeper U-net (fifteen layers) as the backbone network to extract more sufficient breast tumor features. Then, we developed a multi-output U-net to be taken as the bond between the encoder and the decoder to enhance the network adaptability for breast tumors with different scales. Finally, the short-connection based on multi-step down-sampling is used to enhance the correlation of long-range information of encoded features. Extensive experimental results with fifteen state-of-the-art segmentation methods on three public breast ultrasound datasets demonstrate that our method has a more competitive segmentation performance on breast tumors. Furthermore, the robustness of our approach is further illustrated by the segmentation of renal ultrasound images. The source code is publicly available on https://github.com/CGPxy/NU-net.</p