Segmentation of pelvic structures from preoperative images for surgical planning and guidance

Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces

Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer’s disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

In recent years, deep learning has become a breakthrough technique in assisting medical image diagnosis. Supervised learning using convolutional neural networks (CNN) provides state-of-the-art performance and has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Active Learning (AL) methods have been widely applied in natural image classification tasks to reduce annotation costs by selecting more valuable examples from the unlabeled data pool. However, their application in medical image segmentation tasks is limited, and there is currently no effective and universal AL-based method specifically designed for 3D medical image segmentation. To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks. We extensively validated our proposed active learning method on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our PCDAL can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The codes of this study are available at https://github.com/ortonwang/PCDAL

Research of Recycle Bin Forensic Analysis Platform Based On XML Techniques

Conference Name:World Congress on Software Engineering. Conference Address: Xiamen, PEOPLES R CHINA. Time:MAY 19-21, 2009.Windows Recycle Bin is an important component of the operating system and the algorithm is very important and worth exploring. Recycle Bin preserves deleted files and directories signs. So, it's an important part to exam the control files of the Recycle Bin in computer forensic. In this paper, by parsing the structure of INFO2, we completely analyze the recovery model and algorithms of the Recycle Bin. And based on the use of XML structure, the parsing data are packaged in XML. By this way, a practical evidence analysis Platform is designed, which can work out and restore the information of deleted files into the form of a friendly user interface. This application will be an important information-gaining tool to conduct forensic analysis of a suspect's computer system

An Improved Vehicle Detection Algorithm Based on Multi-Intermediate State Machine

The vehicle detection algorithm is an important part of the intelligent transportation system. The accuracy of the algorithm will determine whether accurate vehicle information can be obtained. The system contains several functional modules, including signal amplification, wireless communication, A/D converter, and sensor set/reset functions. To detect all the intersection vehicles, a number of magnetoresistive sensors are connected to the computer system through the wireless communication module, and then, the detected vehicle information will be transferred back to the master host computer. In this paper, two common vehicle detection algorithms, fixed threshold algorithm and adaptive threshold algorithm, were analyzed in the vehicle detection system with magnetoresistive sensors, simultaneously. Finally, an improved multi-intermediate state machine algorithm for vehicle detection was proposed. Using the intermediate state, this algorithm cannot only detect when the vehicle enters the detection area but also decide whether the vehicle leaves the sensor node or not. In this way, it improves the detection accuracy

BP neural network algorithm for multi-sensor trajectory separation based on maximum membership degree

Conference Name:2011 International Conference on Applied Informatics and Communication, ICAIC 2011. Conference Address: Xi'an, China. Time:August 20, 2011 - August 21, 2011.Aiming at the problem of trajectory separation which belongs to the data fusion technology, the multi-sensor trajectory separation algorithm of BP neural network based on the maximum membership degree is presented in this paper. The trajectory points can be predicted by establishing the trajectory prediction model which is based on the BP neural network, and the new radar data can be judged whether they belong to the prescriptive trajectory; Based on the prediction of the BP neural network, the multi-trajectory separation algorithm of fuzzy clustering of maximum membership degree is added to improve the effectiveness and accuracy of the trajectory separation. The experimental tests show that the algorithm which is presented in this paper effectively improves the efficiency and accuracy of the trajectory separation, and has a better application value. Keywords: BP neural network, maximum membership degree, fuzzy clustering, mean square error. ? 2011 Springer-Verlag

Analysis and Implementation of UFS File System Based on Computer Forensics

Conference Name:International Conference on Quantum, Nano and Micro Technologies. Conference Address: Chengdu, PEOPLES R CHINA. Time:OCT 27-28, 2010.UFS is the most important file system of the Unix OS and has been widely applied. Adequate understanding of its working principle and organization structure is the key that the computer forensics been successfully applied to this file system. Based on deep analysis of the system architecture and file access flow, this paper has proposed and realized a technology to analyse UFS file system and explored the data recovery thought. It provides forceful support for computer forensics technology applying to UF