Self-training with dual uncertainty for semi-supervised medical image segmentation

In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem. How to effectively learn image features from unlabeled images to improve segmentation accuracy is the main research direction in this field. Traditional self-training methods can partially solve the problem of insufficient labeled data by generating pseudo labels for iterative training. However, noise generated due to the model's uncertainty during training directly affects the segmentation results. Therefore, we added sample-level and pixel-level uncertainty to stabilize the training process based on the self-training framework. Specifically, we saved several moments of the model during pre-training, and used the difference between their predictions on unlabeled samples as the sample-level uncertainty estimate for that sample. Then, we gradually add unlabeled samples from easy to hard during training. At the same time, we added a decoder with different upsampling methods to the segmentation network and used the difference between the outputs of the two decoders as pixel-level uncertainty. In short, we selectively retrained unlabeled samples and assigned pixel-level uncertainty to pseudo labels to optimize the self-training process. We compared the segmentation results of our model with five semi-supervised approaches on the public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method achieves better segmentation performance on both datasets under the same settings, demonstrating its effectiveness, robustness, and potential transferability to other medical image segmentation tasks. Keywords: Medical image segmentation, semi-supervised learning, self-training, uncertainty estimatio

Ion Doping Effects on the Lattice Distortion and Interlayer Mismatch of Aurivillius-Type Bismuth Titanate Compounds

Taking Bismuth Titanate (Bi4Ti3O12) as a Aurivillius-type compound with m = 3 for example, the ion (W6+/Cr3+) doping effect on the lattice distortion and interlayer mismatch of Bi4Ti3O12 structure were investigated by stress analysis, based on an elastic model. Since oxygen-octahedron rotates in the ab-plane, and inclines away from the c-axis, a lattice model for describing the status change of oxygen-octahedron was built according to the substituting mechanism of W6+/Cr3+ for Ti4+, which was used to investigate the variation of orthorhombic distortion degree (a/b) of Bi4Ti3O12 with the doping content. The analysis shows that the incorporation of W6+/Cr3+ into Bi4Ti3O12 tends to relieve the distortion of pseudo-perovskite layer, which also helps it to become more stiff. Since the bismuth-oxide layer expands while the pseudo-perovskite layer tightens, an analytic model for the plane stress distribution in the crystal lattice of Bi4Ti3O12 was developed from the constitutive relationship of alternating layer structure. The calculations reveal that the structural mismatch of Bi4Ti3O12 is constrained in the ab-plane of a unit cell, since both the interlayer mismatch degree and the total strain energy vary with the doping content in a similar trend to the lattice parameters of ab-plane

Development and Application of Multifunctional Optical Coherence Tomography

Thesis (Ph.D.)--University of Washington, 2014Microcirculation refers to the functions of capillaries and the neighboring lymphatic vessels. It plays a vital role in the pathophysiology of disorders in many clinical areas including cardiology, dermatology, neurology and ophthalmology, and so forth. It is crucial to develop imaging technologies that can provide both qualitative and quantitative information as to how microcirculation responds to certain injury and/or disease, and its treatment. Optical coherence tomography (OCT) is a non-invasive optical imaging technique for high-resolution cross-sectional imaging of specimens, with many applications in clinical medicine. Current state-of-the-art OCT systems operate in the Fourier domain, using either a broadband light source with a spectrometer, known as spectral domain OCT (SDOCT), or a rapidly tunable laser, known as swept source OCT (SSOCT). The current Fourier domain OCT systems have dramatically improvement in sensitivity, resolution and speed compared to time domain OCT. In addition to the improvement in the OCT system hardware, different methods for functional measurements of tissue beds have been developed and demonstrated. This includes but not limited to, i) Phase-resolved Doppler OCT for quantifying the blood flow, ii) OCT angiography for visualization of microvasculature, iii) Polarization sensitive OCT for measuring the intrinsic optical property/ birefringence of tissue, iv) spectroscopic OCT for measuring blood oxygenation, etc. Functional OCT can provide important clinical information that is not available in the typical intensity based structural OCT images. Among these functional OCT modalities, Doppler OCT and OCT angiography attract great interests as they show high capability for in vivo study of microvascular pathology. By analyzing the Doppler effect of a flowing particle on light frequency, Doppler OCT allows the quantification of the blood flow speed and blood flow rate. The most popular approach for Doppler OCT is achieved through analysis of the phase term in complex OCT signal which termed as Phase-resolved Doppler OCT. However, as limited by the phase noise and motion, Phase-resolved Doppler OCT can only be applied for relative large blood vessels, such as arterioles and venules. On the other hand, in order to visualize the microcirculation network, a number of strategies to enable better contrast of microvasculature components, which we termed OCT angiography, have been introduced during recent years. As a variation of Fourier domain OCT, optical microangiography (OMAG) is one of earliest proposed OCT angiography technique which is capable of generating 3D images of dynamic blood perfusion distribution within microcirculatory tissue beds. The OMAG algorithm works by separating the static and moving elements by high pass filtering on complex valued interferometric data after Fourier transform. Based on the conventional OMAG algorithm, we further developed ultra-high sensitive OMAG (UHS-OMAG) by switching the high-pass filtering from fast scan direction (adjacent A-lines within one B-frame) to slow scan direction (adjacent B-frames), which has a dramatically improved performance for capillary network imaging and analysis. Apart from the microvascular study with current available functional OCT for, visualization of the lymphatic system (lymph nodes and lymphatic vessels) plays a significant role in assessing patients with various malignancies and lymphedema. However, there is a lack of label-free and noninvasive method for lymphangiography. Hence, a cutting edge research to investigate the capability of OCT as a tool for non-invasive and label-free lymphangiography would be highly desired. The objective of my thesis is to develop a multiple-functional SDOCT system to image the microcirculation and quantify the several important parameters of microcirculation within microcirculatory tissue beds, and further apply it for pre-clinical research applications. The multifunctional OCT system provides modalities including structural OCT, OCT angiography, Doppler OCT and Optical lymphangiography, for multi-parametric study of tissue microstructure, blood vessel morphology, blood flow and lymphatic vessel all together. The thesis mainly focus on two parts: first, development of multi-functional OCT/optical microangiography (OMAG) system and methods for volumetric imaging of microvasculature and quantitative measurement of blood flow, and its application for pathological research in ophthalmology on rodent eye models; second, development of ultra-high resolution OCT system and algorithm for simultaneous label free imaging of blood and lymphatic vessel, and its application in wound healing study on mouse ear flap model. Objectives of my research are achieved through the following specific aims: Aim 1: Improve the sensitivity of OMAG for microvasculature imaging; perform volumetric and quantitative imaging of vasculature with combined OMAG and Phase-resolved Doppler OCT for in vivo study of vascular physiology. Aim 2: Develop high speed high resolution OCT system and method for rodent eye imaging. Apply the combined OMAG and Phase-resolved Doppler OCT approach to investigate the impact of elevated intraocular pressure on retinal, choroidal and optic nerve head blood flow in rat eye model, which aids to the better understanding of the mechanism and development of glaucoma. Aim 3: Apply the developed OCT system and ultra-high sensitive OMAG algorithm for noninvasive imaging of retinal morphology and microvasculature in obese mice, which may play an important role in early diagnosis of Diabetic retinopathy. Aim 4: Developing an ultra-high resolution SDOCT system using broadband Supercontinuum light source to achieve ultra-high resolution microvasculature imaging of biological tissue. Aim 5: Develop methods for simultaneous label free optical imaging of blood and lymphatic vessel and demonstrate its capability by monitoring the blood and lymph response to wound healing on mouse ear pinna model

Fuzzy logic strategy of prognosticating TCP's timeout and retransmission

The work presented in this paper is the design and implementation of an intelligent strategy using fuzzy logic technology to gauge the TCP timeout and retransmission value. The conventional algorithms, which are based on statistical analysis, perform in a marginally acceptable way for estimating these two values. But they have been shown to be increasingly incapable of dealing with more complicated TCP traffic due to ignorance of traffic complexity. Fuzzy logic technology will be applied to estimate the TCP timeout and retransmission for the purpose of utilising artificial intelligence, combining knowledge about the network traffic and connection

A coupled fuzzy logic control for routers' queue management over TCP/AQM networks

Significant efforts in developing active queue management (AQM) in gateway routers in a TCP/IP network have been made since random early detection (RED) in 1993, and most of them are statistical based. Our approach is to capitalize on the understanding of the TCP dynamics to design an effective AQM scheme. In this paper, two FL-based AQM algorithms are proposed with the deployment of traffic load factor for early congestion notification. Extensive experimental simulations with a range of traffic load conditions have been done for the purpose of performance evaluation. The results show that the proposed two FLAQM algorithms outperform some well-known AQM schemes in terms of both user-centric measures and network-centric measures