Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation

Content-based image retrieval approach was used in our computer-aided detection (CAD) schemes for breast cancer detection with mammography. In this study, we assessed CAD performance and reliability using a reference database including 1500 positive (breast mass) regions of interest (ROIs) and 1500 normal ROIs. To test the relationship between CAD performance and the similarity level between the queried ROI and the retrieved ROIs, we applied a set of similarity thresholds to the retrieved similar ROIs selected by the CAD schemes for all queried suspicious regions, and used only the ROIs that were above the threshold for assessing CAD performance at each threshold level. Using the leave-one-out testing method, we computed areas under receiver operating characteristic (ROC) curves (AZ) to assess CAD performance. The experimental results showed that as threshold increase, (1) less true positive ROIs can be referenced in the database than normal ROIs and (2) the AZ value was monotonically increased from 0.854±0.004 to 0.932±0.016. This study suggests that (1) in order to more accurately detect and diagnose subtle masses, a large and diverse database is required, and (2) assessing the reliability of the decision scores based on the similarity measurement is important in application of the CBIR-based CAD schemes when the limited database is used.

Background. The purpose of this study is to identify a set of features for optimizing the performance of metaphase chromosome detection under high throughput scanning microscopy. In the development of computer-aided detection (CAD) scheme, feature selection is critically important, as it directly determines the accuracy of the scheme. Although many features have been examined previously, selecting optimal features is often application oriented. Methods. In this experiment, 200 bone marrow cells were first acquired by a high throughput scanning microscope. Then 9 different features were applied individually to group captured images into the clinically analyzable and unanalyzable classes. The performance of these different methods was assessed by a receiving operating characteristic (ROC) method. Results. The results show that using the number of labeled regions on each acquired image is suitable for the first on-line CAD scheme. For the second off-line CAD scheme, it would be suggested to combine four feature extraction methods including the number of labeled regions, average regions area, average region pixel value, and the standard deviation of either region distance or circularity. Conclusion. This study demonstrates an effective method of feature selection and comparison to facilitate the optimization of the CAD schemes for high throughput scanning microscope in the future.

Fully Automated Three-Dimensional Detection of Polyps in Fecal-Tagging CT Colonography

Outcome prediction by serum calprotectin in patients with COVID-19 in the emergency department

The authors developed an integrated computer-aided detection (CAD) scheme for detecting and classifying metaphase chromosomes as well as assessing its performance and robustness. This scheme includes an automatic metaphase-finding module and a karyotyping module and it was applied to a testing database with 200 digital microscopic images. The automatic metaphase-finding module detects analyzable metaphase cells using a feature-based artificial neural network (ANN). The ANN-generated outputs are analyzed by a receiver operating characteristics (ROC) method and an area under the ROC curve is 0.966. Then, the automatic karyotyping module classifies individual chromosomes of this cell into 24 types. In this module, a two-layer decision tree-based classifier with eight ANNs established in its connection nodes was optimized by a genetic algorithm. Chromosomes are first classified into seven groups by the ANN in the first layer. The chromosomes in these groups are then separately classified by seven ANNs into 24 types in the second layer. The classification accuracy is 94.5% in the first layer. Six ANNs achieved the accuracy above 95% and only one had lessened performance (80.6%) in the second layer. The overall classification accuracy is 91.5% as compared to 86.7% in the previous study using two independent datasets randomly acquired from our genetic laboratory. The results demonstrate that our automated scheme achieves high and robust performance in identification and classification of metaphase chromosomes.

It is difficult to detect sentinel lymph nodes (SLNs) around an injection point of radiopharmaceuticals mapped in lymphoscintigrams. The purpose of this study was to develop a computer-aided detection (CAD) scheme for SLNs by a subtraction technique using the symmetrical property in the mapped injection point. Our database consisted of 78 lymphoscintigrams with 86 SLNs. In our CAD scheme, the mapped injection point of radiopharmaceuticals was first segmented from the lymphoscintigram using a gray-level thresholding technique. Lymphoscintigram was then divided into four regions by vertical and horizontal straight lines through the center of the segmented injection point. One of the four divided regions was defined as the target region. The correlation coefficients based on pixel values were calculated between the target region and each of the other three regions. The region with the highest correlation coefficient among three regions was selected as the similar region to the target region. The values of pixels on the target region were subtracted by the values of the corresponding pixels on the similar region. This procedure was repeated until every divided region had been used as target region. SLNs were segmented by applying a gray-level thresholding technique to the subtracted image. With our CAD scheme, sensitivity and the number of false positives were 95.3% (82/86) and 2.51 per image, respectively. Our CAD scheme achieved a high level of detection accuracy, and would have a great potential in assisting physicians to detect SLNs in lymphoscintigrams.

The amplification status of human epidermal growth factor receptors 2 (HER2) genes is strongly associated with clinical outcome in patients with breast cancer. The American Society of Clinical Oncology Tumor Marker Guidelines Panel has recommended routine testing of HER2 status on all newly diagnosed metastatic breast cancers since 2001. Although fluorescent in situ hybridization (FISH) technology provides superior accuracy as compared with other approaches, current manual FISH analysis methods are somewhat subjective, tedious, and may introduce interreader variability. The goal of this preliminary study is to develop and test a computer-aided detection (CAD) scheme to assess HER2 status using FISH images. Forty FISH images were selected for this study from our genetic laboratory. The CAD scheme first applies an adaptive, iterative threshold method followed by a labeling algorithm to segment cells of possible interest. A set of classification rules is then used to identify analyzable interphase cells and discard nonanalyzable cells due to cell overlapping and/or other image staining debris (or artifacts). The scheme then maps the detected analyzable cells onto two other gray scale images corresponding to the red and green color of the original image followed by application of a raster scan and labeling algorithms to separately detect the HER-2/neu ("red") and CEP17 ("green") FISH signals. A simple distance based criterion is applied to detect and merge split FISH signals within each cell. The CAD scheme computes the ratio between independent "red" and "green" FISH signals of all analyzable cells identified on an image. If the ratio is ≥ 2.0, the FISH image is assumed to have been acquired from a HER2+ case; otherwise, the FISH image is assumed to have been acquired from HER2- case. When we applied the CAD scheme to the testing dataset, the average computed HER2 amplification ratios were 1.06±0.25 and 2.53±0.81 for HER2- and HER2+ samples, respectively. The results show that the CAD scheme has the ability to automatically detect HER2 status using FISH images. The success of CAD-guided FISH image analysis could result in a more objective, consistent, and efficient approach in determining HER2 status of breast cancers.

Актуальность. Новые возможности улучшения функции внешнего дыхания (ФВД) связаны с применением высокочастотной осцилляции грудной клетки (ВЧОГК), которая позволяет восстановить дренаж бронхиального дерева и оптимизировать легочную вентиляцию. Цель исследования — изучение и оценка ФВД у детей с внебольничной пневмонией (ВП). Материалы и методы. Проведено обследование 107 детей (основная группа (ОГ) — 55 человек и контрольная группа (КГ) — 52 человека) в возрасте 6–17 лет с ВП с острым и неосложненным течением. Дети ОГ получали базисную терапию (БТ) с проведением процедур ВЧОГК, тогда как дети КГ получали исключительно БТ. Исследовали ФВД в динамике лечения, применяя спирометрию. Результаты. В начале терапии ВП показатели ФВД в исследуемых группах имели несущественные различия. При анализе параметров ФВД у детей ОГ на 10-й день БТ отмечено повышение объема форсированного выдоха за 1-ю секунду (ОФВ1) (88,36 ± 1,55 %, p = 0,02), жизненной емкости легких (ЖЕЛ) (88,18 ± 1,53 %, p = 0,02), форсированной жизненной емкости легких (ФЖЕЛ) (86,77 ± 1,37 %, p = 0,03), максимальной объемной скорости воздуха на уровне выдоха 25 % (МОШ25) (90,10 ± 2,99 %, p = 0,02) и максимальной вентиляции легких (МВЛ) (88,31 ± 1,70 %, p = 0,04). Сравнивая показатели ФВД детей КГ, выявили менее выраженную динамику, в частности, ОФВ1 (81,65 ± 2,44 %), ЖЕЛ (82,95 ± 2,56), ФЖЕЛ (80,85 ± 2,09), МОШ25 (82,63 ± 3,08 %) и МВЛ (85,65 ± 1,99 %). При проведении ROC-анализа динамика восстановления ФВД у детей ОГ более заметна за счет улучшения МВЛ, о чем свидетельствует самая большая площадь под кривой ROC (ППК) — 0,99, ОФВ1 — ППК 0,94 и ОФВ1/ФЖЕЛ — ППК 0,94. У детей КГ была менее выражена динамика исследуемых параметров ФВД, что подтверждается МВЛ — ППК 0,63, ОФВ1 — ППК 0,79 и ОФВ1/ФЖЕЛ — ППК 0,89. Выводы. Для улучшения вентиляционной функции легких у детей с ВП необходимо проводить процедуры бронходренажа, используя ВЧОГК в составе БТ, о чем свидетельствуют полученные положительные результаты спирометрии.

To evaluate automatic characterization of a breast MR lesion by its spatially coherent region of interest (ROI). The method delineated 247 enhancing lesions using Otsu thresholding after manually placing a sphere. Mean Shift Clustering subdivided each volume, based on features including pharmacokinetic parameters. An iteratively trained classifier to predict the most suspicious ROI (IsR) was used, to predict the malignancy likelihood of each lesion. Performance was evaluated using receiver operator characteristic (ROC) analysis, and compared with a previous prototype. IsR was compared with noniterative training. The effect of adding BI-RADS™ morphology (from a radiologist) to the classifier was investigated. The area under the ROC curve (AUC) was 0.83 (95% confidence interval [CI] of 0.77-0.88), and was 0.75 (95%CI = 0.68-0.81; P = 0.029) without pharmacokinetic features. IsR performed better than conventional selection, based on one feature (AUC 0.75, 95%CI = 0.68-0.81; P = 0.035). With morphology, the AUC was 0.84 (95%CI = 0.78-0.88) versus 0.82 without (P = 0.40). Breast lesions can be characterized by their most suspicious, contiguous ROI using multi-feature clustering and iterative training. Characterization was improved by including pharmacokinetic modeling, while in our experiments, including morphology did not improve characterization.

Computer-Aided Mass Detection Based on Ipsilateral Multiview Mammograms

Our integrated computer-aided detection (CAD) scheme includes three basic modules. The first module detects whether a microscopic digital image depicts a metaphase chromosome cell. If a cell is detected, the scheme will justify whether it is analyzable with a decision tree. Once an analyzable cell is detected, the second module is applied to segment individual chromosomes and to compute two important features. Specifically, the scheme utilizes a modified thinning algorithm to identify the medial axis of a chromosome. By tracking perpendicular lines along the medial axis, the scheme computes four feature profiles, identifies centromeres, and assigns polarities of chromosomes based on a set of pre-optimized rules. The third module is followed to classify chromosomes into 24 types. In this module, each chromosome is initially represented by a vector of 31 features. A two-layer classifier with 8 artificial neural networks (ANN) is optimized by a genetic algorithm. A testing chromosome is first classified into one of the seven groups by the ANN in the first layer. Another ANN is then automatically selected from the seven ANNs in the second layer (one for each group) to further classify this chromosome into one of 24 types. To test the performance and robustness of this CAD scheme, we randomly selected and assembled an independent testing dataset. The dataset contains 100 microscopic digital images including 50 analyzable and 50 un-analyzable metphase cells identified by the experts. The centromere location, the corresponding polarity, and karyotype for each individual chromosome were recorded in the “truth” file. The performance of the CAD scheme applied to this image dataset is analyzed and compared with the results in the true file. The assessment accuracies are 93% for the first module, 90.8% for centromere identification and 93.2% for polarity assignment in the second module, over 96% for six chromosome groups and 81.8% for one group in the third module, respectively. These accuracy levels are very comparable with those achieved during our previous studies to develop and optimize these CAD modules. Hence, the study demonstrates that our automated scheme can achieve high and robust performance in identification and classification of metaphase chromosomes. Keywords: Metaphase chromosomes, Computer-aided detection, Karyotyping, Performance assessment

Objective To explore the value of quantitative T2 weighted magnetic resonance imaging (T2WI) and diffusion tensor imaging (DTI) parameters for predicting functional outcomes after surgery for cervical spondylotic myelopathy (CSM). Methods One hundred and forty CSM patients received T2WI and DTI before surgery at Tianjin Hospital between April 2014 and April 2016. They were then given systematic rehabilitation treatment after the surgery. The Japanese Orthopaedics Association (JOA) scoring system was applied to evaluate their neurological function before and after the surgery. According to the JOA recovery rate at 1-year follow-up, the patients were divided into a good recovery group (recovery rate≥50%) and a poor recovery group (recovery rate <50%). Maximum spinal cord compression (MSCC), signal change ratio (SCR), transverse area (TA), apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were compared between the two groups. Receiver operating characteristics (ROC) curves were used to measure the prognostic ability and determine the best cut-off value for each variable. The independent predictors of a poor recovery were estimated using univariate and multivariate analysis. Results ROC analysis showed that the area under the ROC curve (AUC) of MSCC was 0.593. For SCR it was 0.682. For the TA it was 0.706. For the ADC it was 0.719 and for FA it was 0.749. The respective cut-off values were 44% compression, 1.561, 0.46 cm2, 1.339×10-3 mm2/s and 0.386. FA had the largest AUC, followed by ADC, TA, SCR and MSCC. MSCC and SCR had low discrimination power (AUC<0.7) in predicting a poor recovery, whereas TA, ADC and FA had moderate discrimination power (AUC 0.7-0.9). Logistic multivariate regression showed that a low JOA score, TA≤0.46 cm2 or FA≤0.386 were independent risk factors for a poor recovery. A predicting model built according to the results of the logistic regression analysis gave an AUC of 0.87, significantly better than that of the JOA score. With a cut-off value of 0.36, the sensitivity and specificity were 80% and 77% respectively. Conclusions Combining T2WI and DTI parameters with the JOA score may better predict the recovery of patients with CSM. The values can also provide references for making up rehabilitation plans. Key words: T2 weighted magnetic resonance imaging; Diffusion tensor imaging; Cervical vertebrae; Spinal cord compression; Prognosis

Restriction spectrum imaging (RSI), as an advanced quantitative diffusion-weighted magnetic resonance imaging technique, has the potential to distinguish primary benign and malignant lung lesions. To explore how well the tri-compartmental RSIperforms in distinguishing primary benign from malignant lung lesions compared with diffusion-weighted imaging (DWI), and to further explore whether positron emission tomography/magnetic resonance imaging (PET/MRI) can improve diagnostic efficacy. Prospective. 137 patients, including 108 malignant and 29 benign lesions (85 males, 52 females; average age = 60.0 ± 10.0 years). T2WI, T1WI, multi-b value DWI, MR-based attenuation correction, and PET imaging on a 3.0 T whole-body PET/MR system. The apparent diffusion coefficient (ADC), RSI-derived parameters (restricted diffusion , hindered diffusion , and free diffusion ) and the maximum standardized uptake value (SUVmax) were calculated and analyzed for diagnostic efficacy individually or in combination. Student's t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curves, Delong test, Spearman's correlation analysis. P < 0.05 was considered statistically significant. The , SUVmax were significantly higher, and , ADC were significantly lower in the malignant group [0.717 ± 0.131, 9.125 (5.753, 13.058), 0.194 ± 0.099, 1.240 (0.972, 1.407)] compared to the benign group [0.504 ± 0.236, 3.390 (1.673, 6.030), 0.398 ± 0.195, 1.485 ± 0.382]. The area under the ROC curve (AUC) values ranked from highest to lowest as follows: AUC (SUVmax) > AUC ( ) > AUC ( ) > AUC (ADC) > AUC ( ) (AUC = 0.819, 0.811, 0.770, 0.745, 0549). The AUC (AUC = 0.900) of the combined model of RSI with PET was significantly higher than that of either single-modality imaging. RSI-derived parameters ( , ) might help to distinguish primary benign and malignant lung lesions and the discriminatory utility of was not observed. The RSI exhibits comparable or potentially enhanced performance compared with DWI, and the combined RSI and PET model might improve diagnostic efficacy. Stage 2.

BackgroundThermal imaging (TI) is a portable, low cost imaging tool with high feasibility for use. Clinical joint assessment.Is routinely performed in rheumatoid arthritis (RA) patient care.ObjectivesTo assess a combined TI...

Ratio of visceral fat area to body fat mass (VBR) is a superior predictor of coronary heart disease.