Progress in TN staging of rectal cancer based on multimodal magnetic resonance imaging / 国际生物医学工程杂志

Rectal cancer is one of the most common gastrointestinal malignancies in China. Accurate and reasonable assessment of the preoperative staging of rectal cancer can significantly enhance treatment outcomes and improve patient prognosis. Magnetic resonance imaging is the technique of choice for local staging of rectal cancer and has significant advantages in the diagnosis of rectal primary tumors (T) and peri-intestinal lymph nodes (N). In this review paper, the research ideas and progress of traditional radiomics and deep learning methods for preoperative TN staging prediction of rectal cancer were reviewed around multimodal magnetic resonance images, with the aim of providing new ideas for realizing fully automated TN staging algorithms for rectal cancer.

Application of generative adversarial network in magnetic resonance image reconstruction / 生物医学工程学杂志

Magnetic resonance imaging (MRI) is an important medical imaging method, whose major limitation is its long scan time due to the imaging mechanism, increasing patients' cost and waiting time for the examination. Currently, parallel imaging (PI) and compress sensing (CS) together with other reconstruction technologies have been proposed to accelerate image acquisition. However, the image quality of PI and CS depends on the image reconstruction algorithms, which is far from satisfying in respect to both the image quality and the reconstruction speed. In recent years, image reconstruction based on generative adversarial network (GAN) has become a research hotspot in the field of magnetic resonance imaging because of its excellent performance. In this review, we summarized the recent development of application of GAN in MRI reconstruction in both single- and multi-modality acceleration, hoping to provide a useful reference for interested researchers. In addition, we analyzed the characteristics and limitations of existing technologies and forecasted some development trends in this field.

Segmentation of ground glass pulmonary nodules using full convolution residual network based on atrous spatial pyramid pooling structure and attention mechanism / 生物医学工程学杂志

Accurate segmentation of ground glass nodule (GGN) is important in clinical. But it is a tough work to segment the GGN, as the GGN in the computed tomography images show blur boundary, irregular shape, and uneven intensity. This paper aims to segment GGN by proposing a fully convolutional residual network, i.e., residual network based on atrous spatial pyramid pooling structure and attention mechanism (ResAANet). The network uses atrous spatial pyramid pooling (ASPP) structure to expand the feature map receptive field and extract more sufficient features, and utilizes attention mechanism, residual connection, long skip connection to fully retain sensitive features, which is extracted by the convolutional layer. First, we employ 565 GGN provided by Shanghai Chest Hospital to train and validate ResAANet, so as to obtain a stable model. Then, two groups of data selected from clinical examinations (84 GGN) and lung image database consortium (LIDC) dataset (145 GGN) were employed to validate and evaluate the performance of the proposed method. Finally, we apply the best threshold method to remove false positive regions and obtain optimized results. The average dice similarity coefficient (DSC) of the proposed algorithm on the clinical dataset and LIDC dataset reached 83.46%, 83.26% respectively, the average Jaccard index (IoU) reached 72.39%, 71.56% respectively, and the speed of segmentation reached 0.1 seconds per image. Comparing with other reported methods, our new method could segment GGN accurately, quickly and robustly. It could provide doctors with important information such as nodule size or density, which assist doctors in subsequent diagnosis and treatment.

Progress in computer-assisted Alberta stroke program early computer tomography score of acute ischemic stroke based on different modal images / 生物医学工程学杂志

Clinically, non-contrastive computed tomography (NCCT) is used to quickly diagnose the type and area of ​​stroke, and the Alberta stroke program early computer tomography score (ASPECTS) is used to guide the next treatment. However, in the early stage of acute ischemic stroke (AIS), it's difficult to distinguish the mild cerebral infarction on NCCT with the naked eye, and there is no obvious boundary between brain regions, which makes clinical ASPECTS difficult to conduct. The method based on machine learning and deep learning can help physicians quickly and accurately identify cerebral infarction areas, segment brain areas, and operate ASPECTS quantitative scoring, which is of great significance for improving the inconsistency in clinical ASPECTS. This article describes current challenges in the field of AIS ASPECTS, and then summarizes the application of computer-aided technology in ASPECTS from two aspects including machine learning and deep learning. Finally, this article summarizes and prospects the research direction of AIS-assisted assessment, and proposes that the computer-aided system based on multi-modal images is of great value to improve the comprehensiveness and accuracy of AIS assessment, which has the potential to open up a new research field for AIS-assisted assessment.

3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks / 浙江大学学报（英文版）（B辑：生物医学和生物技术）

To overcome the computational burden of processing three-dimensional (3D) medical scans and the lack of spatial information in two-dimensional (2D) medical scans, a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2D convolutional neural networks (2D-CNNs). In order to combine the low-level features and high-level features, we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process. Further, in order to resolve the problems of the blurred boundary of the glioma edema area, we superimposed and fused the T2-weighted fluid-attenuated inversion recovery (FLAIR) modal image and the T2-weighted (T2) modal image to enhance the edema section. For the loss function of network training, we improved the cross-entropy loss function to effectively avoid network over-fitting. On the Multimodal Brain Tumor Image Segmentation Challenge (BraTS) datasets, our method achieves dice similarity coefficient values of 0.84, 0.82, and 0.83 on the BraTS2018 training; 0.82, 0.85, and 0.83 on the BraTS2018 validation; and 0.81, 0.78, and 0.83 on the BraTS2013 testing in terms of whole tumors, tumor cores, and enhancing cores, respectively. Experimental results showed that the proposed method achieved promising accuracy and fast processing, demonstrating good potential for clinical medicine.

Research progress on genetic effects of the asymmetry of human brain structure revealed by magnetic resonance imaging / 生物医学工程学杂志

Hemispheric asymmetry is a fundamental organizing principle of the human brain. Answering the genetic effects of the asymmetry is a prerequisite for elucidating developmental mechanisms of brain asymmetries. Multi-modal magnetic resonance imaging (MRI) has provided an important tool for comprehensively interpreting human brain asymmetry and its genetic mechanism. By combining MRI data, individual differences in brain structural asymmetry have been investigated with quantitative genetic brain mapping using gene-heritability. Twins provide a useful natural model for studying the effects of genetics and environment on the brain. Studies based on MRI have found that the asymmetry of human brain structure has a genetic basis. From the perspective of quantitative genetic analysis, this article reviews recent findings on the genetic effects of asymmetry and genetic covariance between hemispheres from three aspects: the asymmetry of heritability, the heritability of asymmetry and the genetic correlation. At last, the article shows the limitations and future research directions in this field. The purpose of this systematic review is to quickly guide researchers to understand the origins and genetic mechanism of interhemispheric differences, and provide a genetic basis for further understanding and exploring individual differences in laterized cognitive behavior.

Application of deep learning in cancer prognosis prediction model / 生物医学工程学杂志

In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.

Research advances in multi-modality medical image registration and fusion methods and their clinical application / 中华放射肿瘤学杂志

Multi?modality medical image processing has become a hot topic for research in the field of image processing and plays an important role in clinical diagnosis and treatment. Images with different modalities provide different information on patients. Anatomical images ( such as computed tomography and magnetic resonance imaging ) provide information on anatomical morphology and the structure of human body, and functional images ( such as single?photon emission computed tomography and positron emission tomography) provide the functional information on the distribution of radioactive concentration within human body. Such information needs to be fused to obtain comprehensive fusion images, and the images with different modalities need to be registered to obtain useful fusion images. This article reviews several image registration and fusion techniques used in the medical field, points out their advantages and shortcomings, and introduces the application of various processing techniques in clinical practice.

An Improved Empirical Mode Decomposition Algorithm for Phonocardiogram Signal De-noising and Its Application in S1/S2 Extraction / 生物医学工程学杂志

In this paper, an improved empirical mode decomposition (EMD) algorithm for phonocardiogram (PCG) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. Firstly, by applying EMD-Wavelet algorithm for pre-processing, the PCG signal was well filtered. Then, the filtered PCG signal was saved and applied in the following processing steps. Secondly, time domain features, frequency domain features and energy envelope of the each intrinsic mode function's (IMF) were computed. Based on the time frequency domain features of PCG's IMF components which were extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components were pinpointed accurately. Meanwhile, a detecting fixed method, which was based on the time domain processing, was proposed to amend the detection results. Finally, to test the performance of the algorithm proposed in this paper, a series of experiments was contrived. The experiments with thirty samples were tested for validating the effectiveness of the new method. Results of test experiments revealed that the accuracy for recognizing S1/S2 components was as high as 99.75%. Comparing the results of the method proposed in this paper with those of traditional algorithm, the detection accuracy was increased by 5.56%. The detection results showed that the algorithm described in this paper was effective and accurate. The work described in this paper will be utilized in the further studying on identity recognition.

A probability segmentation algorithm for lung nodules based on three-dimensional features / 生物医学工程学杂志

This paper presents a probability segmentation algorithm for lung nodules based on three-dimensional features. Firstly, we computed intensity and texture features in region of interest (ROI) pixel by pixel to get their feature vector, and then classified all the pixels based on their feature vector. At last, we carried region growing on the classified result, and got the final segmentation result. Using the public Lung Imaging Database Consortium (LIDC) lung nodule datasets, we verified the performance of proposed method by comparing the probability map within LIDC datasets, which was drawn by four radiology doctors separately. The experimental results showed that the segmentation algorithm using three-dimensional intensity and texture features would be effective.

A review on the research progress of the computer-aided detection of pulmonary nodule / 生物医学工程学杂志

Computer-aided detection (CAD) of pulmonary nodule technology can effectively assist the radiologist to enhance lung nodule detection efficiency and accuracy rate, so it can lay the foundation for the early diagnosis of lung cancer. In order to provide reference for the scholars and to develop the CAD technology, we in this paper review the technology research and development of CAD of the pulmonary nodules which is based on CT image in recent years both home and abroad. At the same time, we also analyse the advantages and shortcomings of different methods. Then we present the improvement direction for reference. According to the literature in recent years, there still has been large development space in CAD technology for pulmonary nodules. The establishment and improvement of the CAD system in each step would be of great scientific value.

A practical multi-modality medical image registration solution / 生物医学工程学杂志

In medical imaging field, doctors often complete the intra-subject registration of multi modality images by choosing a pair of anatomic landmarks. It is hard to choose the same landmark accurately in different imaging modality. For multi-modality image registration, mutual information measure is widely used because it suffers little from the difference among different modalities, but it has disadvantages of large amount of calculation and slow converges. In this paper, considering the convenience in practice, we firstly register different modality images by choosing a pair of corresponding landmarks. In order to decrease the error during choosing the landmarks, we further do the registration using mutual information method. The advantage of this solution is that the landmark based registration presents a good starting point for further mutual information (MI) registration, while the MI method decreases the difficulty of choosing landmarks. Experimental results showed that this registration solution was fast, accurate, and would have a good clinical potential application in the future.

Progress on Medical Image Registration and its Application / 中国医学物理学杂志

Objective: To summarize the major progress in medical image registration in recent years. Furthermore, based on the recent advances in this field, this paper can provide a reference in following domains: three-dimensional medical image reconstruction, medical image visualization, quantitative analysis. Methods: Firstly, referring to a myriad of latest papers on medical image registration. Secondly, analyzing traits and exiting problems of techniques which presented in those papers. Finally, putting forward some efficient methods for solving these problems. Results: This paper compares the characteristics of some typical algorithms and its application and looks forward to the future research work. Conclusion: Using optimization strategy to improve the quality of image registration and studying on non-rigid image registration are the directions for future research in medical image registration field.

Software Design of Beds Control System Test Platform Based on PCI / 中国医学物理学杂志

Objective: To design a upper computer software which can achieve data acquisition, display, motion control for sickbed-control test system. Methods: In Visual C++6.0 environment, take advantage of Advantech's development kit, and integrate multi-thread and dual-buffer technology to achieve. PC translate user's intentions into control commands, then sent commands to PCI1240, PCI1716 through the PCI interface, PCI1240 drive stepper motor to control the movement of the bed, while PCI1716 collect movement state information. Results: Movement can be stopped immediately by clicking the stop button even during the reciprocating motion, and solve screen flicker when drawing the real-time curves. The software has been test in bed-control system many times and achieved good results. Conclusions: This paper's method realized the sickbed's motion control, data acquisition, data storage and display, compared with the method that using single chip machine and general electromotor, our method makes bed movement more precise and smooth, more function are achieved, and the software has been successfully used in the sickbed-control system.

The experimental research on the frameless registration based on the digital subtraction angiography / 生物医学工程学杂志

In this paper, we present an experimental research on the frameless registration of DSA/CT images based on frameless localization algorithm. The result shows that, 3D fusion and registration of vessels in the DSA images and anatomical structures in CT images will help surgeons to make accurate diagnosis and on plann operative.