MyoV: a deep learning-based tool for the automated quantification of muscle fibers

Accurate approaches for quantifying muscle fibers are essential in biomedical research and meat production. In this study, we address the limitations of existing approaches for hematoxylin and eosin-stained muscle fibers by manually and semiautomatically labeling over 660 000 muscle fibers to create a large dataset. Subsequently, an automated image segmentation and quantification tool named MyoV is designed using mask regions with convolutional neural networks and a residual network and feature pyramid network as the backbone network. This design enables the tool to allow muscle fiber processing with different sizes and ages. MyoV, which achieves impressive detection rates of 0.93-0.96 and precision levels of 0.91-0.97, exhibits a superior performance in quantification, surpassing both manual methods and commonly employed algorithms and software, particularly for whole slide images (WSIs). Moreover, MyoV is proven as a powerful and suitable tool for various species with different muscle development, including mice, which are a crucial model for muscle disease diagnosis, and agricultural animals, which are a significant meat source for humans. Finally, we integrate this tool into visualization software with functions, such as segmentation, area determination and automatic labeling, allowing seamless processing for over 400 000 muscle fibers within a WSI, eliminating the model adjustment and providing researchers with an easy-to-use visual interface to browse functional options and realize muscle fiber quantification from WSIs.

LabGym: Quantification of user-defined animal behaviors using learning-based holistic assessment

Quantifying animal behavior is important for biological research. Identifying behaviors is the prerequisite of quantifying them. Current computational tools for behavioral quantification typically use high-level properties such as body poses to identify the behaviors, which constrains the information available for a holistic assessment. Here we report LabGym, an open-source computational tool for quantifying animal behaviors without this constraint. In LabGym, we introduce "pattern image" to represent the animal's motion pattern, in addition to "animation" that shows all spatiotemporal details of a behavior. These two pieces of information are assessed holistically by customizable deep neural networks for accurate behavior identifications. The quantitative measurements of each behavior are then calculated. LabGym is applicable for experiments involving multiple animals, requires little programming knowledge to use, and provides visualizations of behavioral datasets. We demonstrate its efficacy in capturing subtle behavioral changes in diverse animal species.

Towards an Enhanced Tool for Quantifying the Degree of LV Hyper-Trabeculation

Left ventricular non-compaction (LVNC) is defined by an increase of trabeculations in left ventricular (LV) endomyocardium. Although LVNC can be in isolation, an increase in hypertrabeculation often accompanies genetic cardiomyopathies. Current methods for quantification of LV trabeculae have limitations. Several improvements are proposed and implemented to enhance a software tool to quantify the trabeculae degree in the LV myocardium in an accurate and automatic way for a population of patients with genetic cardiomyopathies (QLVTHCI). The software tool is developed and evaluated for a population of 59 patients (470 end-diastole cardiac magnetic resonance images). This tool produces volumes of the compact sector and the trabecular area, the proportion between these volumes, and the left ventricular and trabeculated masses. Substantial enhancements are obtained over the manual process performed by cardiologists, so saving important diagnosis time. The parallelization of the detection of the external layer is proposed to ensure real-time processing of a patient, obtaining speed-ups from 7.5 to 1500 with regard to QLVTHCI and the manual process used traditionally by cardiologists. Comparing the method proposed with the fractal proposal to differentiate LVNC and non-LVNC patients among 27 subjects with previously diagnosed cardiomyopathies, QLVTHCI presents a full diagnostic accuracy, while the fractal criteria achieve 78%. Moreover, QLTVHCI can be installed and integrated in hospitals on request, whereas the high cost of the license of the fractal method per year of this tool has prevented reproducibility by other medical centers.

A Novel Automatic Approach for Calculation of the Specific Binding Ratio in [I-123]FP-CIT SPECT

A fully automatic method for specific binding ratio (SBR) calculation in [¹²³I]ioflupane single-photon emission computed tomography (SPECT) studies was proposed by creating volumes of interest of the striatum (VOI_st) and reference region (VOI_ref) without manual handling to avoid operator-induced variability. The study involved 105 patients (72 ± 10 years) suspected of parkinsonian syndrome (PS) who underwent [¹²³I]ioflupane SPECT. The 200 images from our previous study were used for evaluation and validation of the new program. All patients were classified into PS and non-PS groups according to the results of clinical follow-up. A trapezoidal volume of interest (VOI_t) containing all striatal intensive counts was created automatically, followed by VOI_st setting using the previous method. SBR values were calculated from the mean values of VOI_st and VOI_ref determined by the whole brain outside of VOI_t. The low count voxels in the VOI_ref were excluded using an appropriate threshold. The SBR values from the new method were compared with the previous semi-automatic method and the Tossici–Bolt (TB) method. The SBRs from the semi- and fully automatic methods showed a good linear correlation (r > 0.98). The areas under the curves (AUCs) of receiver operating characteristic analysis showed no significant difference between the two methods for both our previous (AUC > 0.99) and new (AUC > 0.95) data. The diagnostic accuracy of the two methods showed similar results (>92%), and both were better than the TB method. The proposed method successfully created the automatic VOIs and calculated SBR rapidly (9 ± 1 s/patient), avoiding operator-induced variability and providing objective SBR results.

In Silico Approach for Immunohistochemical Evaluation of a Cytoplasmic Marker in Breast Cancer

Breast cancer is the most frequently diagnosed cancer in women and the second most common cancer overall, with nearly 1.7 million new cases worldwide every year. Breast cancer patients need accurate tools for early diagnosis and to improve treatment. Biomarkers are increasingly used to describe and evaluate tumours for prognosis, to facilitate and predict response to therapy and to evaluate residual tumor, post-treatment. Here, we evaluate different methods to separate Diaminobenzidine (DAB) from Hematoxylin and Eosin (H&E) staining for Wnt-1, a potential cytoplasmic breast cancer biomarker. A method comprising clustering and Color deconvolution allowed us to recognize and quantify Wnt-1 levels accurately at pixel levels. Experimental validation was conducted using a set of 12,288 blocks of m×n pixels without overlap, extracted from a Tissue Microarray (TMA) composed of 192 tissue cores. Intraclass Correlations (ICC) among evaluators of the data of 0.634, 0.791, 0.551 and 0.63 for each Allred class and an average ICC of 0.752 among evaluators and automatic classification were obtained. Furthermore, this method received an average rating of 4.26 out of 5 in the Wnt-1 segmentation process from the evaluators.

Fast left ventricle tracking using localized anatomical affine optical flow

In daily clinical cardiology practice, left ventricle (LV) global and regional function assessment is crucial for disease diagnosis, therapy selection, and patient follow-up. Currently, this is still a time-consuming task, spending valuable human resources. In this work, a novel fast methodology for automatic LV tracking is proposed based on localized anatomically constrained affine optical flow. This novel method can be combined to previously proposed segmentation frameworks or manually delineated surfaces at an initial frame to obtain fully delineated datasets and, thus, assess both global and regional myocardial function. Its feasibility and accuracy were investigated in 3 distinct public databases, namely in realistically simulated 3D ultrasound, clinical 3D echocardiography, and clinical cine cardiac magnetic resonance images. The method showed accurate tracking results in all databases, proving its applicability and accuracy for myocardial function assessment. Moreover, when combined to previous state-of-the-art segmentation frameworks, it outperformed previous tracking strategies in both 3D ultrasound and cardiac magnetic resonance data, automatically computing relevant cardiac indices with smaller biases and narrower limits of agreement compared to reference indices. Simultaneously, the proposed localized tracking method showed to be suitable for online processing, even for 3D motion assessment. Importantly, although here evaluated for LV tracking only, this novel methodology is applicable for tracking of other target structures with minimal adaptations.

Computed tomography quantification of tracheal abnormalities in COPD and their influence on airflow limitation

PURPOSE

To present a method to automatically quantify tracheal morphology changes during breathing and investigate its contribution to airflow impairment when adding CT measures of emphysema, airway wall thickness, air trapping and ventilation.

METHODS

Because tracheal abnormalities often occur localized, a method is presented that automatically determines the most abnormal trachea section based on automatically computed sagittal and coronal lengths. In this most abnormal section, trachea morphology is encoded using four equiangular rays from the center of the trachea and the normalized lengths of these rays are used as features in a classification scheme. Consequently, trachea measurements are used as input for classification into GOLD stages in addition to emphysema, air trapping and ventilation. A database of 200 subjects distributed across all GOLD stages is used to evaluate the classification with a k nearest neighbour algorithm. Performance is assessed in two experimental settings: (a) when only inspiratory scans are taken; (b) when both inspiratory and expiratory scans are available.

RESULTS

Given only an inspiratory CT scan, measuring tracheal shape provides complementary information only to emphysema measurements. The best performing set in the inspiratory setting was a combination of emphysema and bronchial measurements. The best performing feature set in the inspiratory-expiratory setting includes measurements of emphysema, ventilation, air trapping, and trachea. Inspiratory and inspiratory-expiratory settings showed similar performance.

CONCLUSIONS

The fully automated system presented in this study provides information on trachea shape at inspiratory and expiratory CT. Addition of tracheal morphology features improves the ability of emphysema and air trapping CT-derived measurements to classify COPD patients into GOLD stages and may be relevant when investigating different aspects of COPD.

Comparison of the diagnostic accuracies of very low stress-dose with standard-dose myocardial perfusion imaging: Automated quantification of one-day, stress-first SPECT using a CZT camera

BACKGROUND

Previous studies have demonstrated accurate diagnosis of reduced dose myocardial perfusion imaging (MPI) using Cadmium-Zinc-Telluride (CZT) technology. We compared the diagnostic performances of very low stress-dose (<2 mSv) with standard-dose stress-first, quantitative MPI using a CZT camera.

METHODS

Patients without known coronary artery- disease who underwent a stress-first Tc-99 m sestamibi CZT-MPI and invasive coronary angiography (ICA), and low-risk patients without ICA were included. A stress-rest standard-dose (10/30 mCi) MPI and a low-dose (5/15 mCi) MPI were compared. Normal limits for quantification were developed from 40 (20 males) low-risk patients, and total perfusion deficit (TPD) was derived.

RESULTS

208 patients who underwent MPI and ICA, and 76 low-risk patients were included. Of these, 128 had a standard-dose MPI and 156 had a low-dose MPI. Stress-doses in low-dose and standard-dose groups were 5.9 ± 1.2 vs 10.2 ± 0.5 mCi (1.7 ± 0.3 vs 3.0 ± 0.1 mSv), respectively, P < 0.001, and stress-rest effective radiation was 6.9 ± 1.1 vs 11.7 ± 0.4 mSv, respectively, P < 0.001. Sensitivity, specificity, and accuracy values in the low-dose and standard-dose groups were 86.1%, 76.6%, and 81.4%; and 90.6%, 78.1%, and 84.4%, respectively, P = ns. Using TPD prone, specificity values were 84.9% and 80.3%, respectively, P = ns.

CONCLUSION

One-day stress-first MPI with 50% radiation reduction and a very low stress-dose (<2 mSv) using CZT technology and quantitative supine and prone analysis provided a high diagnostic value, similar to standard-dose MPI.

Automatic quantification of microvessel density in urinary bladder carcinoma

Seventy-three TUR-T biopsies from bladder carcinoma were evaluated regarding microvessel density, defined as microvessel number (nMVD) and cross-section endothelial cell area (aMVD). A semi-automatic and a newly developed, automatic image analysis technique were applied in immunostainings, performed according to an optimized staining protocol. In 12 cases a comparison of biopsy material and the corresponding cystectomy specimen were tested, showing a good correlation in 11 of 12 cases (92%). The techniques proved reproducible for both nMVD and aMVD quantifications related to total tumour area. However, the automatic method was dependent on high immunostaining quality. Simultaneous, semi-automatic quantification of microvessels, stroma and epithelial fraction resulted in a decreased reproducibility. Quantification in ten images, selected in a descending order of MVD by subjective visual judgement, showed a poor observer capacity to estimate and rank MVD. Based on our results we propose quantification of MVD related to one tissue compartment. When staining quality is of high standard, automatic quantification is applicable, which facilitates quantification of multiple areas and thus, should minimize selection variability.

Automatic quantification of immunohistochemically stained cell nuclei based on standard reference cells

A fully automatic method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions, is presented. Agarose embedded cultured fibroblasts were fixed, paraffin embedded and sectioned at 4 microm. They were then stained together with 4 microm sections of the test specimen obtained from bladder cancer material. A colour based classifier is automatically computed from the control cells. The method was tested on formalin fixed paraffin embedded tissue section material, stained with monoclonal antibodies against the Ki67 antigen and cyclin A protein. Ki67 staining results in a detailed nuclear texture with pronounced nucleoli and cyclin A staining is obtained in a more homogeneously distributed pattern. However, different staining patterns did not seem to influence labelling index quantification, and the sensitivity to variations in light conditions and choice of areas within the control population was low. Thus, the technique represents a robust and reproducible quantification method. In tests measuring proportions of stained area an average standard deviation of about 1.5% for the same field was achieved when classified with classifiers created from different control samples.