Improvement prostate cancer detection rate of suspicious lesions through MRI/TRUS fusion guided biopsy by a multiteam of radiologists

Background The objective of our study was to analyze the data of our biopsies, determine a detection rate (DR), compare it with the data in the literature and draw possible deductions, so as to offer the patient the possibility of not having other biopsies in the future. Methods We have enrolled 189 biopsy-naive patients in the period between September 2018 and December 2020. Each patient underwent multiparametric (mp)-MRI which was reviewed by our team of radiologists. In our center, each examination is examined by 4 radiologists separately with an overall final result. Through the t student test, any statistically significant differences between the DRs and the concordance rate between the positive cores and the suspected area on MRI were analyzed for each urologist who performed the procedure. Results The absolute (DR) was 69.3% (131/189 patients). The relative DR for each PIRADS score was 41% for PIRADS 3, 70.2% for PIRADS 4, 89.3% for PIRADS 5. We found a high percentage of agreement between the positive biopsy samples and the suspicious area identified on MRI: 90.8% (119/131 patients). There were no statistically significant differences between the DRs of the urologists who performed the procedure (p = 0.89), nor for the percentage of agreement between the positivity of the core and the suspected area on MRI (p = 0.92). Conclusions MRI in the future could become the gold standard for performing MRI fusion-guided biopsies to have a better diagnostic result and avoid rebiopsies. A team MRI reading allows greater accuracy in identifying the suspected lesion, which is demonstrated by a high rate of agreement with the positivity of the cores (90.8%). There is a cost problem due to the need to carry out the mpMRI but it could have less impact in the future. In addition, the MRI provides useful information on the extent of the disease (e.g., cT3a/b) which allows you to better plan the surgical strategy or other therapies.

A Review of Underwater Mine Detection and Classification in Sonar Imagery

Underwater mines pose extreme danger for ships and submarines. Therefore, navies around the world use mine countermeasure (MCM) units to protect against them. One of the measures used by MCM units is mine hunting, which requires searching for all the mines in a suspicious area. It is generally divided into four stages: detection, classification, identification and disposal. The detection and classification steps are usually performed using a sonar mounted on a ship’s hull or on an underwater vehicle. After retrieving the sonar data, military personnel scan the seabed images to detect targets and classify them as mine-like objects (MLOs) or benign objects. To reduce the technical operator’s workload and decrease post-mission analysis time, computer-aided detection (CAD), computer-aided classification (CAC) and automated target recognition (ATR) algorithms have been introduced. This paper reviews mine detection and classification techniques used in the aforementioned systems. The author considered current and previous generation methods starting with classical image processing, and then machine learning followed by deep learning. This review can facilitate future research to introduce improved mine detection and classification algorithms.

Research on a Location Method for Complex Voltage Sag Sources Based on Random Matrix Theory

The precise location of voltage sag sources plays an important role in formulating a voltage sag governance plan and clarifying the responsibility for the accident. The traditional location method of voltage sag sources is difficult to establish a precise mathematical model and locate the complex voltage sag sources accurately in the complex distribution network. In order to solve these problems, a location method for complex voltage sag sources based on random matrix theory is proposed in this paper. Firstly, the augmented matrix is constructed based on the influence factor data and the operation state data of the distribution network as the data source matrix, and the statistical characteristics of each data are analyzed by using the random matrix theory to determine the suspicious area of voltage sag source. Then, the disturbance signal of each node in the suspicious area of voltage sag source is analyzed by using the atomic algorithm and disturbance active power method, and it can determine the location of each disturbance source in the complex voltage sag event and the cause of voltage sag at each node. Compared with the existing model-based methods, the proposed method in this paper is a data-driven approach, which does not need the physical model and topology information. Furthermore, it can reduce the amount of data and improve the analysis efficiency on the basis of determining the suspicious area of the voltage sag source. Finally, the examples are given to show that the proposed method can accurately locate the disturbance sources in complex voltage sag events.

Biopsy guided by dermoscopy in cutaneous pigmented lesion - Case report

It may be clinically difficult to differentiate early-stage melanoma from benign tumors, specially pigmented seborrheic keratosis. Dermoscopy can help; however, the findings are not always conclusive. Therefore, histopathology may be necessary for a correct diagnosis. We describe a melanocytic lesion with dubious clinic and dermoscopic findings. An incisional biopsy of a suspicious area, guided by dermoscopy, was performed to clarify the findings.

Research on the Algorithm of Prevention Forest Fire Disaster in the Poyang Lake Ecological Economic Zone

Poyang Lake Ecological Economic Zone has large forest area, so it's very important to construct prevention forest fire disaster system. This paper presents an algorithm for prevention forest fire disaster based on digital image processing technology. The algorithm distinguishes the realtime forest video by smoke and fire. To determine whether there are some suspicious area in the image in the spatial domain by judging the color properties of smoke and fire through Clustering Algorithm. If it detects any suspicious circumstances, then fixes ccd and detects the suspicious areas in the time domain. In this step, firstly get the initial detect results by wavelet decomposition , then use the k-means clustering algorithm for the spread detection of smoke. Experimental results show that the algorithm is ideal for the experimental video. It alarms before the fire disaster occurs to avoid major fire disaster, which protects the forest resources in the Poyang Lake Ecological Economic Zone.

SEGMENTATION OF MASS IN MAMMOGRAMS USING A NOVEL INTELLIGENT ALGORITHM

In order to improve the performance of mass segmentation on mammograms, an intelligent algorithm is proposed in this paper. It establishes two mass models to characterize the various masses, and the ones in the denser tissue are represented with Model I, while the ones in the fatty tissue are represented with Model II. Then, it uses iterative thresholding to extract the suspicious area, as well as the rough regions of those masses matching Model II, and applies a DWT-based technique to locate those masses matching Model I, which are hidden in the high gray-level intensity and contrast area. A region growing process restricted by Canny edge detection is subsequently used to segment the rough regions of those masses matching Model I, and finally snakes are carried out to find all the mass regions roughly extracted above. Thirty patient cases with 60 mammograms and 107 masses were used for evaluation, and the experimental result has demonstrated the algorithm's better performance over the conventional methods.