Automatic Region Of Interest Research Articles

Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study.

The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultrasound backscattered radiofrequency (RF) signals is emerging for tissue characterization as the RF signals carry abundant information related to tissue microstructures. However, the existing methods only used the time-domain information of the RF signals for liver fibrosis assessment, and the liver region of interest (ROI) is outlined manually. In this study, we proposed an approach for liver fibrosis assessment using deep learning models on ultrasound RF signals. The proposed method consisted of two-dimensional (2D) convolutional neural networks (CNNs) for automatic liver ROI segmentation from reconstructed B-mode ultrasound images and one-dimensional (1D) CNNs for liver fibrosis stage classification based on the frequency spectra (amplitude, phase, and power) of the segmented ROI signals. The Fourier transform was used to obtain the three kinds of frequency spectra. Two classical 2D CNNs were employed for liver ROI segmentation: U-Net and Attention U-Net. ROI spectrum signals were normalized and augmented using a sliding window technique. Ultrasound RF signals collected (with a 3-MHz transducer) from 613 participants (Group A) were included for liver ROI segmentation and those from 237 participants (Group B) for liver fibrosis stage classification, with a liver biopsy as the reference standard (Fibrosis stage: F0 = 27, F1 = 49, F2 = 51, F3 = 49, F4 = 61). In the test set of Group A, U-Net and Attention U-Net yielded Dice similarity coefficients of 95.05% and 94.68%, respectively. In the test set of Group B, the 1D CNN performed the best when using ROI phase spectrum signals to evaluate liver fibrosis stages ≥F1 (area under the receive operating characteristic curve, AUC: 0.957; accuracy: 89.19%; sensitivity: 85.17%; specificity: 93.75%), ≥F2 (AUC: 0.808; accuracy: 83.34%; sensitivity: 87.50%; specificity: 78.57%), and ≥F4 (AUC: 0.876; accuracy: 85.71%; sensitivity: 77.78%; specificity: 94.12%), and when using the power spectrum signals to evaluate ≥F3 (AUC: 0.729; accuracy: 77.14%; sensitivity: 77.27%; specificity: 76.92%). The experimental results demonstrated the feasibility of both the 2D and 1D CNNs in liver parenchyma detection and liver fibrosis characterization. The proposed methods have provided a new strategy for liver fibrosis assessment based on ultrasound RF signals, especially for early fibrosis detection. The findings of this study shed light on deep learning analysis of ultrasound RF signals in the frequency domain with automatic ROI segmentation.

Phantomless Computed Tomography-Based Quantitative Bone Mineral Density Assessment: A Literature Review

The global surge in aging populations has intensified osteoporosis challenges, necessitating improved diagnostic methods. While dual X-ray absorptiometry (DXA) is the conventional standard in assessing bone mineral density (BMD), the exploration of quantitative computed tomography (QCT) has been proposed, particularly phantomless techniques. Challenges in single-energy CT, emphasizing internal calibration standards like subcutaneous fat, are discussed. Advances in PL-QCT, notably with automatic region of interest (ROI) selection, show improved accuracy. Dual-energy CT (DECT) introduces new dimensions for musculoskeletal analysis. Despite advancements, challenges persist, including interindividual variability and patient-specific factors. Evolving single-energy CT and DECT techniques show promise in refining BMD assessment and osteoporosis diagnosis, enhancing patient care. Continued research and integration into clinical practice are vital for realizing these advancements’ full benefits. In this review, we evaluate and summarize current evidence on the feasibility and different approaches to achieve analysis of BMD with phantomless QCT.

Object Detection and Regression Based Visible Spectrophotometric Analysis: A Demonstration Using Methylene Blue Solution

This study investigates the estimation of the concentration of methylene blue solutions to understand if visible spectrophotometry could be performed using a smartphone and machine learning. The presented procedure consists of taking photos, detecting test tubes and sampling region of interest (ROI) with YOLOv5, finding the hue, saturation, value (HSV) code of the dominant color in the ROI, and regression. 257 photos were taken for the procedure. The YOLOv5 object detection architecture was trained on 928 images and the highest mAP@05 values were detected as 0.915 in 300 epochs. For automatic ROI sampling, the YOLOv5 detect.py file was edited. The trained YOLOv5 detected 254 out of 257 test tubes and extracted ROIs. The HSV code of the dominant color in the exported ROI images was determined and stored in a csv file together with the concentration values. Subsequently, 25 different regression algorithms were applied to the generated data set. The extra trees regressor was the most generalizing model with 99.5% training and 99.4% validation R2 values. A hyperparameter tuning process was performed on the extra trees regressor and a mixed model was created using the best 3 regression algorithms to improve the R2 value. Finally, all three models were tested on unseen data and the lowest MSE value was found in the untuned extra trees regressor and blended model with values of 0.10564 and 0.16586, respectively. These results prove that visible spectrophotometric analysis can be performed using the presented procedure and that a mobile application can be developed for this purpose.

A robust watermarking approach against high‐density salt and pepper noise (RWSPN) to enhance medical image security

AbstractThis paper proposes a robust watermarking method against high‐density salt and pepper noise attacks. Automatic region of interest (ROI) detection, embedding encoded data, removing different densities of noise, data extraction by omitting, and labeling the noisy pixels of Region of Non‐Interest (RONI), and decoding the extracted data using ROI pixel information as a key, are various steps of the presented scheme. The automatic ROI detection method separates the RONI from ROI with four vertices of the smallest rectangle, for the embedding process. The encoded watermark data is embedded into the least significant bits of RONI in four neighbour pixels. Adaptive Removal of high‐density Salt and pepper Noise method can enhance image quality and reduce the effect of salt and pepper noise attacks. The embedded information is preserved from destruction if the host image is impaired through the power of robustness. The best results are obtained through the action of extracting the watermark from RONI pixels, utilizing the same ROI detection method. Omitting and labeling the noisy pixels of the RONI will ensure healthy extracted watermark data, leading to decreased Bit Error Rate (BER) values. Finally, these data are interpreted using the key of ROI pixels, and the watermark data is decoded and retrieved. Due to salt and pepper noise obliterating pixel bits and their corresponding transform coefficients in the transform domain, the spatial domain is employed to enhance robustness against such attacks. The results show the high performance of the presented scheme. The average BER value for five MRI databases in a 97% salt and pepper noise attack is 38.6.

Gamma camera-specific reference standards for radioactive iodine uptake measurements

BackgroundCurrent guidelines of the radioiodine uptake (RAIU) test allow the use of different equipment, isotopes, activity and region-of-interest (ROI). We evaluated presence and extent of these differences in clinical practice and evaluated the effect of some of these variations on RAIU outcomes. Also, gamma camera-specific reference standards were calculated and retrospectively compared with measurements obtained during clinical RAIU tests.Materials and methodsFirst, questionnaires were sent to Dutch nuclear medicine departments requesting information about equipment usage, isotope, isotope formulation, activity and measurement techniques. Secondly, a neck phantom containing a range of activities in capsule or water-dissolved formulation was scanned. Counts were measured using automatic ROI, square box ROI or all counts in the image. Thirdly, clinical RAIU data were collected during 2015–2018 using three different gamma cameras. Reference standards for each scanner were calculated using regression analysis between reference activity and measured counts. Uptake measurements using this gamma camera-specific reference standard were compared with original measurements.ResultsThe survey demonstrated significant differences in isotope, isotope formulation, activity, use of neck phantoms, frequency and duration of reference measurements, distance to collimator, use of background measurements and ROI delineation. The phantom study demonstrated higher counts for the water-dissolved formulation than capsules using both automatic and square box ROI. Also, higher counts were found using a square box ROI than an automatic ROI. The retrospective study showed feasibility of RAIU calculations using camera-specific reference standards and good correlation with the original RAIU measurements.ConclusionsThis study demonstrated considerable technical variation in RAIU measurement in clinical practice. The phantom study demonstrated that these differences could result in differences in count measurements, potentially resulting in different dose calculations for radioactive iodine therapy. Retrospective data suggest that camera-specific reference standards may be used instead of individual reference measurements using separate activity sources, which may thus eliminate some sources of variation.

A deep learning-based detection method for pig body temperature using infrared thermography

Body temperature is one of the important indicators that reflect the health of pigs. The traditional rectal temperature measurement is inconvenient and time-consuming, and also easy to cause stress responses to pigs. Infrared Thermography (ITG) was supposed to have potentials to realize non-invasive and rapid temperature detection for pigs in intensive pig farming. In this paper, an automatic temperature detection method base on ITG was developed. First, temperatures on six regions on pig body surface (i.e., forehead (FH), eyes, nose, ear root (ET), back and anus) were measured by ITG for the region of interest (ROI) selection; Then, an improved model of YOLOv5s-BiFPN was developed for automatic ROI detection and temperature extraction. A dataset with 2797 images that collected from sixteen pigs for 30 days was used for model development. It was shown that temperatures on FH and ET were supposed to be the ROI for ITG temperature detection and because of their strong correlations with the rectal temperature among the six parts on pig body surface. Also, the maximum temperature (MaxT) on FH and ET could reflect pig’s body temperature variations the best. The proposed model of YOLOv5s-BiFPN achieved optimal performances (e.g., the mAP was 96.36%, outperformance was 20 MB, target detection speed was up to 100 frame/s), and the mAPs were increased by 4.85%, 4.38%, 1.60%, 1.56%, 31.52%, and 22.42% compared with models of CenterNet, Faster R-CNN, YOLOv4, YOLOv5s, Nanodet and YOLOv5n, respectively. Therefore, it is a feasible way for pig body temperature automatic detection and facilitates early staged disease warning and environmental control.

Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images

Selecting regions of interest (ROI) is a common step in medical image analysis across all imaging modalities. An ROI is a subset of an image appropriate for the intended analysis and identified manually by experts. In modern pathology, the analysis involves processing multidimensional and high resolution whole slide image (WSI) tiles automatically with an overwhelming quantity of structural and functional information. Despite recent improvements in computing capacity, analyzing such a plethora of data is challenging but vital to accurate analysis. Automatic ROI detection can significantly reduce the number of pixels to be processed, speed the analysis, improve accuracy and reduce dependency on pathologists. In this paper, we present an ROI detection method for WSI and demonstrated it for human epidermal growth factor receptor 2 (HER2) grading for breast cancer patients. Existing HER2 grading relies on manual ROI selection, which is tedious, time-consuming and suffers from inter-observer and intra-observer variability. This study found that the HER2 grade changes with ROI selection. We proposed an ROI detection method using Vision Transformer and investigated the role of image magnification for ROI detection. This method yielded an accuracy of 99% using 20 × WSI and 97% using 10 × WSI for the ROI detection. In the demonstration, the proposed method increased the diagnostic agreement to 99.3% with the clinical scores and reduced the time to 15 seconds for automated HER2 grading.

Comparison of MTFs Measured using IndoQCT and ImQuest Software on GE CT Phantom Images

Purpose: This study aims to compare the modulation transfer function (MTF) measured using IndoQCT and ImQuest software on GE CT phantom images with variations of slice thickness and reconstruction filter. Method: This study compared MTFs measured using two software (i.e., IndoQCT and ImQuest software) on GE CT phantom images. IndoQCT implemented automatic region of interest (ROI) determination, while ImQuest used manual ROI determination. Both software analysed images with three variations of slice thickness (i.e., 2.5, 5, and 10 mm) and reconstruction filter (i.e., standard, soft tissue, chest, and bone filters). Results: IndoQCT provides automatic ROI selection and produces stable MTF curves, while ImQuest provides manual ROI selection and produces more fluctuating MTF curves. In slice thickness variation, IndoQCT showed that different slice thickness did not significantly affect spatial resolution as suggested, while ImQuest showed significant differences in spatial resolution for thickest slice thickness. In filter variation, IndoQCT produced high spatial resolution value in bone filter, while ImQuest produced the lower spatial resolution value. Overall, both software showed MTF differences within 15%, except in certain conditions, for example on a slice thickness of 10 mm. Conclusions: We have compared MTFs measured using IndoQCT and ImQuest software. Both software can be used to measure MTFs on the GE phantom images with relatively accurate results. IndoQCT generally produces higher MTF values than those from ImQuest. However, the difference MTF is not statistically significant. This study found that at 10 mm slice thickness, ImQuest produced very low MTF, while IndoQCT produced stable results for different slice thicknesses and reconstruction filters.

Automatic ROI recognition and parameters selection for digital image correlation in measuring structures with complex shapes

For the digital image correlation (DIC) method, the measurement of specimens with complex shapes may encounter difficulties due to the time-consuming recognition of region of interest (ROI), and the indeterminate parameter selection caused by the non-uniform deformation. This paper proposes an automatic DIC for the measurement of structures with complex shapes. An automatic ROI segmentation is developed by combining a convolutional neural network and image morphology, so the boundary of the specimen can be acquired accurately and efficiently. In dealing with the non-uniform deformation, a strain-related automatic selection of DIC parameters is developed, in which the sampling intervals and the subset sizes at different areas can be automatically determined. Both results of the simulated experiment and real experiment show that, by combing the two approaches with segmentation-aided DIC, the proposed automatic DIC can characterize the complex deformation including the boundary of the structures effectively.

Deep learning-based automatic left atrial appendage filling defects assessment on cardiac computed tomography for clinical and subclinical atrial fibrillation patients

Rationale and objectivesSelecting region of interest (ROI) for left atrial appendage (LAA) filling defects assessment can be time consuming and prone to subjectivity. This study aimed to develop and validate a novel artificial intelligence (AI), deep learning (DL) based framework for automatic filling defects assessment on CT images for clinical and subclinical atrial fibrillation (AF) patients. Materials and methodsA total of 443,053 CT images were used for DL model development and testing. Images were analyzed by the AI framework and expert cardiologists/radiologists. The LAA segmentation performance was evaluated using Dice coefficient. The agreement between manual and automatic LAA ROI selections was evaluated using intraclass correlation coefficient (ICC) analysis. Receiver operating characteristic (ROC) curve analysis was used to assess filling defects based on the computed LAA to ascending aorta Hounsfield unit (HU) ratios. ResultsA total of 210 patients (Group 1: subclinical AF, n = 105; Group 2: clinical AF with stroke, n = 35; Group 3: AF for catheter ablation, n = 70) were enrolled. The LAA volume segmentation achieved 0.931–0.945 Dice scores. The LAA ROI selection demonstrated excellent agreement (ICC ≥0.895, p < 0.001) with manual selection on the test sets. The automatic framework achieved an excellent AUC score of 0.979 in filling defects assessment. The ROC-derived optimal HU ratio threshold for filling defects detection was 0.561. ConclusionThe novel AI-based framework could accurately segment the LAA region and select ROIs while effectively avoiding trabeculae for filling defects assessment, achieving close-to-expert performance. This technique may help preemptively detect the potential thromboembolic risk for AF patients.

Optimised generalized polarisation analysis of C-laurdan reveals clear order differences between T cell membrane compartments

Heterogenous packing of plasma membrane lipids is important for cellular processes like signalling, adhesion and sorting of membrane components. Solvatochromic membrane fluorophores that respond to changes from liquid-ordered (lo) phase to liquid-disordered (ld) by red shifts in their emission spectra are often used to assess lipid packing. Their response can be quantified using generalized polarisation (GP) using fluorescence microscopy images from two emission ranges, preferably from a region of interest (ROI) limited to a specific membrane compartment. However, image quality is limited by Poisson noise and convolution by the point spread function of the imaging system. Examining GP-analysis of C-laurdan labelled T cells using the image restoration procedure deconvolution, we demonstrate that deconvolution substantially improves the image resolution by making the plasma membrane clearly discernible and facilitating plasma membrane ROI selection. We conclude that automatic ROI selection has advantages over manual ROI selection when it comes to reproducibility and speed, but reliable GP-measurements can also be obtained by manually demarcated ROIs. We find that deconvolution enhances the difference in GP-values between the plasma and intracellular membranes and demonstrate that moving an intensity defined plasma membrane ROI outwards from the cell further improves this differentiation. By systematically changing the key deconvolution regularization parameter signal to noise, we establish a protocol for deconvolution optimisation applicable to any solvatochromic dye and imaging system. The image processing and ROI selection protocol presented improves both the resolution and precision of GP-measurement and will enable detection of smaller changes in membrane order than is currently achievable.

Direct Gene Expression Profile Prediction for Uveal Melanoma from Digital Cytopathology Images via Deep Learning and Salient Image Region Identification

To demonstrate that deep learning (DL) methods can produce robust prediction of gene expression profile (GEP) in uveal melanoma (UM) based on digital cytopathology images. Evaluation of a diagnostic test or technology. Deidentified smeared cytology slides stained with hematoxylin and eosin obtained from a fine needle aspirated from UM. Digital whole-slide images were generated by fine-needle aspiration biopsies of UM tumors that underwent GEP testing. A multistage DL system was developed with automatic region-of-interest (ROI) extraction from digital cytopathology images, an attention-based neural network, ROI feature aggregation, and slide-level data augmentation. The ability of our DL system in predicting GEP on a slide (patient) level. Data were partitioned at the patient level (73% training; 27% testing). In total, our study included 89 whole-slide images from 82 patients and 121 388 unique ROIs. The testing set included 24 slides from 24 patients (12 class 1 tumors; 12 class 2 tumors; 1 slide per patient). Our DL system for GEP prediction achieved an area under the receiver operating characteristic curve of 0.944, an accuracy of 91.7%, a sensitivity of 91.7%, and a specificity of 91.7% on a slide-level analysis. The incorporation of slide-level feature aggregation and data augmentation produced a more predictive DL model (P= 0.0031). Our current work established a complete pipeline for GEP prediction in UM tumors: from automatic ROI extraction from digital cytopathology whole-slide images to slide-level predictions. Our DL system demonstrated robust performance and, if validated prospectively, could serve as an image-based alternative to GEP testing.

12-APR Segmentation and Global Hu-F Descriptor for Human Spine MRI Image Retrieval

The image retrieval system has been used to provide the needed correct images to the physicians while the diagnosis and treatment process is being conducted. The earlier image retrieval system was a text-based image retrieval system (TBIRS) that used keywords for the image context and it requires human’s help to manually make text annotation on the images. The text annotation process is a laborious task especially when dealing with a huge database and is prone to human errors. To overcome the aforementioned issues, the approach of a content-based image retrieval system (CBIRS) with automatic indexing using visual features such as colour, shape and texture becomes popular. Thus, this study proposes a semi-automated shape segmentation method using a 12-anatomical point representation method of the human spine vertebrae for CBIRS. The 12 points, which are annotated manually on the region of interest (ROI), is followed by automatic ROI extraction. The segmentation method performs excellently, as evidenced by the highest accuracy of 0.9987, specificity of 0.9989, and sensitivity of 0.9913. The features of the segmented ROI are extracted with a novel global Hu-F descriptor that combines a global shape descriptor, a Hu moment invariant, and a Fourier descriptor based on the ANOVA selection approach. The retrieval phase is implemented using 100 MRI data of the human spine for thoracic, lumbar, and sacral bones. The highest obtained precision is 0.9110 using a normalized Manhattan metric for lumbar bones. In a conclusion, a retrieval system to retrieve lumbar bones of the MRI human spine has been successfully developed to help radiologists in diagnosing human spine diseases.

The Effects of Automatic Segmentations on Preoperative Lymph Node Status Prediction Models With Ultrasound Radiomics for Patients With Early Stage Cervical Cancer.

Introduction: The purpose of this study is to investigate the effects of automatic segmentation algorithms on the performance of ultrasound (US) radiomics models in predicting the status of lymph node metastasis (LNM) for patients with early stage cervical cancer preoperatively. Methods: US images of 148 cervical cancer patients were collected and manually contoured by two senior radiologists. The four deep learning-based automatic segmentation models, namely U-net, context encoder network (CE-net), Resnet, and attention U-net were constructed to segment the tumor volumes automatically. Radiomics features were extracted and selected from manual and automatically segmented regions of interest (ROIs) to predict the LNM of these cervical cancer patients preoperatively. The reliability and reproducibility of radiomics features and the performances of prediction models were evaluated. Results: A total of 449 radiomics features were extracted from manual and automatic segmented ROIs with Pyradiomics. Features with an intraclass coefficient (ICC) > 0.9 were all 257 (57.2%) from manual and automatic segmented contours. The area under the curve (AUCs) of validation models with radiomics features extracted from manual, attention U-net, CE-net, Resnet, and U-net were 0.692, 0.755, 0.696, 0.689, and 0.710, respectively. Attention U-net showed best performance in the LNM prediction model with a lowest discrepancy between training and validation. The AUCs of models with automatic segmentation features from attention U-net, CE-net, Resnet, and U-net were 9.11%, 0.58%, –0.44%, and 2.61% higher than AUC of model with manual contoured features, respectively. Conclusion: The reliability and reproducibility of radiomics features, as well as the performance of radiomics models, were affected by manual segmentation and automatic segmentations.

Non-Contact Respiration Monitoring and Body Movements Detection for Sleep Using Thermal Imaging.

Monitoring of respiration and body movements during sleep is a part of screening sleep disorders related to health status. Nowadays, thermal-based methods are presented to monitor the sleeping person without any sensors attached to the body to protect privacy. A non-contact respiration monitoring based on thermal videos requires visible facial landmarks like nostril and mouth. The limitation of these techniques is the failure of face detection while sleeping with a fixed camera position. This study presents the non-contact respiration monitoring approach that does not require facial landmark visibility under the natural sleep environment, which implies an uncontrolled sleep posture, darkness, and subjects covered with a blanket. The automatic region of interest (ROI) extraction by temperature detection and breathing motion detection is based on image processing integrated to obtain the respiration signals. A signal processing technique was used to estimate respiration and body movements information from a sequence of thermal video. The proposed approach has been tested on 16 volunteers, for which video recordings were carried out by themselves. The participants were also asked to wear the Go Direct respiratory belt for capturing reference data. The result revealed that our proposed measuring respiratory rate obtains root mean square error (RMSE) of bpm. The advantage of this approach lies in its simplicity and accessibility to serve users who require monitoring the respiration during sleep without direct contact by themselves.

Human-Perception-Oriented Pseudo Analog Video Transmissions With Deep Learning

Recently, pseudo analog transmission has gained increasing attentions due to its ability to alleviate the cliff effect in video multicast scenarios. The existing pseudo analog systems are sorely optimized under the minimum mean squared error criterion without taking the perceptual video quality into consideration. In this paper, we propose a human-perception-based pseudo analog video transmission system named ROIC-Cast, which aims to intelligently enhance the transmission quality of the region-of-interest (ROI) parts. Firstly, the classic deep learning based saliency detection algorithm is adopted to decompose the continuous video sequences into ROI and non-ROI blocks. Secondly, an effective compression method is used to reduce the data amount of side information generated by the ROI extraction module. Then, the power allocation scheme is formulated as a convex problem, and the optimal transmission power for both ROI and non-ROI blocks is derived in a closed form. Finally, the simulations are conducted to validate the proposed system by comparing with a few of existing systems, e.g., KMV-Cast, SoftCast, and DAC-RAN. The proposed ROIC-Cast can achieve over 4.1dB peak signal- to-noise ratio gains of ROI compared with other systems, given the channel signal-to-noise ratio as -5dB, 0dB, 5dB, and 10dB, respectively. This significant performance improvement is due to the automatic ROI extraction, high-efficiency data compression as well as adaptive power allocation.

Automatic Striatal ROI Delineation for Semi-quantitative Analysis of 99mTc-TRODAT-1 Brain SPECT Imaging

Single-photon emission computed tomography (SPECT) imaging, a relatively inexpensive nuclear imaging technique, has been used as a diagnostic tool for Parkinson’s disease (PD). The tracer 99mTc-TRODAT-1 is commonly used in Taiwan, where the imaging cost is covered under the national health insurance system. However, poor TRODAT-SPECT imaging resolution is a problem. Therefore, we developed a semi-quantitative program that can perform automatic region of interest (ROI) delineation for semi-quantifying striatal dopamine transporter activities. A total of 228 subjects were enrolled in this study, including PD patients (n = 62) and healthy controls (n = 166). The subjects received a 99mTc-TRODAT-1 brain SPECT scan. Some of the subjects (n = 40) also received a high-resolution magnetic resonance imaging (MRI) scan for the evaluation of the accuracy of the semi-quantitative assessment. In addition, the developed automatic ROI delineation method was used to calculate the striatal specific uptake ratio (SUR) in dopamine transporter imaging. The reproducibility of the SUR obtained using the method was compared to those in previous studies. Based on linear regression analysis (r2 = 0.92, correlation coefficient = 0.96), a correlation was found between the results obtained using the automatic ROI delineation method and those of the manual method in striatum with the corresponding MR images. The age-related decline of SUR availability measured in this study was 6.4% per decade. There was almost no difference between our results and those in related clinical studies. The proposed automatic ROI delineation program for SUR analysis was tested and used to estimate the ROIs in TRODAT-SPECT images to aid PD diagnosis. The program could be helpful for physicians who are inexperienced with TRODAT-SPECT images.

Small target detection based on automatic ROI extraction and local directional gray&entropy contrast map

As a special problem in object recognition, how to detect the small target from complex background fast and precisely is kept an open topic. An excellent algorithm should have ideal Pd (Probability of detection) as well as lower Fa (False alarm) provided that no missing detection. Taking it as the goal, in this paper, we put forward an enhanced version based on LDG&ECM (Local Directional Gray&Entropy Contrast Map). To enhance the discrimination of the target with the background clutter, the saliency feature of “contrast” is remearsured by associating the “gray intensity” with “entropy”. Also, with the introduction of “directivity” in the calculation, the stubborn clutter edge with similar property to the target is removed effectively. Moreover, to speed up the algorithm, the cGANs (Conditional Generative Adversarial Networks) is first introduced to extract ROI (Region of Interest) automatically. Based on DNNs, the traditional extraction methods relying on empirical threshold is improved to an end-to-end way. Additional, to detect the multiple targets in heterogeneous background, the original image is segmented into multiple homogeneous parts by OTSU. And then, a novel local adaptive threshold decision making is designed. By this way, all of the real targets with prominent local extreme property are detected and the false alarm caused by local extreme is avoided due to the balance of global information. It is verified that the proposed automation and adaptively approach has significant performance improvement compared with the state-of-the-art algorithms in various scenarios.

ROI Extraction Based on Multiview Learning and Attention Mechanism for Unbalanced Remote Sensing Data Set

With an increasing number of remote sensing images (RSIs), the automatic region of interest (ROI) extraction based on convolutional neural networks (CNNs) has attracted much interest in recent years. Although fully supervised CNN-based methods have shown superiority in the field of object extraction, pixelwise annotations are expensive and time-consuming. Moreover, due to the unstable distribution of ROIs in complex landscapes, the ratios of foreground and background areas are quite different in RSIs. Training CNNs with such unbalanced data sets lead to over-fitting and low accuracy. In this article, we propose a framework that combines multiview learning and attention mechanism (MLAM) to solve the above mentioned problems. First, we develop a CNN-based weakly supervised method with a weight-balanced loss function to solve the problems caused by an unbalanced data set. It also helps to generate imagewise saliency maps by computing the gradient maps with respect to the input images. Then, we design a multiview strategy to dramatically reduce the missing inspection. Finally, we design a feedback attention mechanism based on the stage neighbor binary pattern to further modify the extraction result. In summary, the proposed framework achieves pixelwise ROI extraction under imagewise annotations through data-driven ML and a knowledge-driven visual attention mechanism. We evaluate the performance of the MLAM framework on two challenging data sets with complex backgrounds. The experimental results indicate that the proposed framework can achieve better performance than other eight ROI extraction models for unbalanced remote sensing data sets.

Measurement of effective renal plasma flow using model analysis of dynamic CT in the preoperative evaluation of the renal transplant donors.

Renal scintigraphy is widely used to evaluate residual function of a transplanted kidney from the donor. Dynamic computed tomography (CT) imaging can evaluate both kidney morphology and regional renal function. The aim of this study was to develop an imaging protocol and a calculation method using dynamic CT for assessing the effective renal plasma flow (ERPF) by model analysis, and to evaluate the validity of the obtained ERPF values. Preoperative dynamic CT examination with a low radiation dose exposure system was performed for 25 renal transplant donors, and ERPF was calculated from the obtained images (CT-ERPF). To calculate CT-ERPF, we set the region of interest (ROI) in the renal cortex using automatic ROI-setting software developed in our laboratory. We compared the processing time with automatic and manual ROI settings. To evaluate the validity of CT-ERPF, we examined the correlation of age with CT-ERPF and compared with reported ERPF values. We also compared the uptake rates of technetium-99m-dimercaptosuccinic acid and CT-ERPF in terms of the right-to-left ratio. There was good agreement of CT-ERPF assessed using automatic and manual ROIs. CT-ERPF was negatively correlated with age and showed values below the reference ERPF range in 21 cases. The right-to-left ratio of CT-ERPF showed a significant correlation with that of technetium-99m-dimercaptosuccinic acid. Using our method, CT-ERPF was a useful indicator for preoperative evaluation of donor's renal function.