Automatic Bone Marrow Cellularity Estimation in H&E Stained Whole Slide Images.

Bone marrow cellularity is an important measure in diagnostic hematopathology. Currently, the gold standard for bone marrow cellularity estimation is manual inspection of hematoxylin and eosin stained whole slide images (H&E WSI) by hematopathologists. However, these assessments are subjective and subject to interobserver and intraobserver variability. This may be reduced by using a computer-assisted estimate of bone marrow cellularity. The aim of this study was to develop a fully automated algorithm to estimate bone marrow cellularity in H&E WSI stains using bone marrow segmentation. Data consisted of eight bone marrow H&E WSIs extracted from eight subjects. An algorithm was developed to estimate the bone marrow cellularity consisting of biopsy segmentation, tissue classification, and bone marrow segmentation. Segmentations of the red and yellow bone marrow (YBM) were used to estimate the bone marrow cellularity within the WSI H&E stains. The DICE coefficient between automatic tissue segmentations and ground truth segmentations conducted by an experienced hematopathologist were used for validation. Furthermore, the agreement between the automatic and two manual cellularity estimates was assessed using Bland-Altman plots and intraclass correlation coefficients (ICC). The validation of the bone marrow segmentation demonstrated an average DICE of 0.901 and 0.920 for the red and YBM, respectively. A mean cellularity estimate difference of -0.552 and - 7.816 was obtained between the automatic cellularity estimates and two manual cellularity estimates, respectively. An ICC of 0.980 (95%CI: 0.925-0.995, P-value: 5.51 × 10-7 ) was obtained between the automatic and manual cellularity estimates based on manual annotations. The study demonstrated that it was possible to obtain bone marrow cellularity estimates with a good agreement with bone marrow cellularity estimates obtained from an experienced hematopathologist. © 2019 International Society for Advancement of Cytometry.

Quality assessment of Ki67 staining using cell line proliferation index and stain intensity features.

Breast cancer is the most frequent cancer among women worldwide. Ki67 can be used as an immunohistochemical pseudo marker for cell proliferation to determine how aggressive the cancer is and thereby the treatment of the patient. No standard Ki67 staining protocol exists, resulting in inter-laboratory stain variability. Therefore, it is important to determine the quality control of a staining protocol to ensure correct diagnosis and treatment of patients. Currently, quality control is performed by the organization NordiQC that use an expert panel-based qualitative assessment system. However, no objective method exists to determine the quality of a staining protocol. In this study, we propose an algorithm, to objectively assess staining quality from segmented cell nuclei structures extracted from cell lines. The cell nuclei were classified into either Ki67 positive or negative to determine the Ki67 proliferation index within the cell lines. A Ki67 stain quality model based on ordinal logistic regression was developed to determine the quality of a staining protocol from features extracted from the segmented cell nuclei in the cell lines. The algorithm was able to segment and classify Ki67 positive cell nuclei with a sensitivity and positive predictive value (PPV) of 0.90 and 0.94 and Ki67 negative cell nuclei with a sensitivity and PPV of 0.78 and 0.78. The mean difference between a manual and automatic Ki67 proliferation index was -0.003 with a standard deviation of 0.056. The ordinal logistic regression model found that the stain intensity for both the Ki67 positive and Ki67 negative cell nuclei were statistically significant as parameters determining the stain quality from the cell line cores. The framework shows great promise for using cell nuclei information from cell lines to predict the staining quality of staining protocols. © 2018 International Society for Advancement of Cytometry.

Using cell nuclei features to detect colon cancer tissue in hematoxylin and eosin stained slides.

Currently, diagnosis of colon cancer is based on manual examination of histopathological images by a pathologist. This can be time consuming and interpretation of the images is subject to inter- and intra-observer variability. This may be improved by introducing a computer-aided diagnosis (CAD) system for automatic detection of cancer tissue within whole slide hematoxylin and eosin (H&E) stains. Cancer disrupts the normal control mechanisms of cell proliferation and differentiation, affecting the structure and appearance of the cells. Therefore, extracting features from segmented cell nuclei structures may provide useful information to detect cancer tissue. A framework for automatic classification of regions of interest (ROI) containing either benign or cancerous colon tissue extracted from whole slide H&E stained images using cell nuclei features was proposed. A total of 1,596 ROI's were extracted from 87 whole slide H&E stains (44 benign and 43 cancer). A cell nuclei segmentation algorithm consisting of color deconvolution, k-means clustering, local adaptive thresholding, and cell separation was performed within the ROI's to extract cell nuclei features. From the segmented cell nuclei structures a total of 750 texture and intensity-based features were extracted for classification of the ROI's. The nine most discriminative cell nuclei features were used in a random forest classifier to determine if the ROI's contained benign or cancer tissue. The ROI classification obtained an area under the curve (AUC) of 0.96, sensitivity of 0.88, specificity of 0.92, and accuracy of 0.91 using an optimized threshold. The developed framework showed promising results in using cell nuclei features to classify ROIs into containing benign or cancer tissue in H&E stained tissue samples. © 2017 International Society for Advancement of Cytometry.

Peroperative epicardial ultrasonography of distal coronary artery bypass graft anastomoses using a stabilizing device. A feasibility study.

BACKGROUND: Widespread use of intraoperative epicardial ultrasonography (ECUS) for quality assessment of coronary artery bypass graft anastomoses during coronary artery bypass grafting (CABG) has not occurred - presumably due to technological and practical challenges including the need to maintain stable and optimal acoustic contact between the ultrasound probe and the target without the risk of distorting the anastomosis. We investigated the feasibility of using a stabilizing device during ultrasound imaging of distal coronary bypass graft anastomoses in patients undergoing on-pump CABG. Imaging was performed in both the longitudinal and transverse planes. METHODS: Single-centre, observational prospective feasibility study among 51 patients undergoing elective, isolated on-pump CABG. Ultrasonography of peripheral coronary bypass anastomoses was performed using a stabilizing device upon which the ultrasound transducer was connected. Transit-time flow measurement (TTFM) was also performed. Descriptive statistical tests were used. RESULTS: Longitudinal and transverse images from the heel, middle and toe were obtained from 134 of 155 coronary anastomoses (86.5%). After the learning curve (15 patients), all six projections were obtained from 100 of 108 anastomoses scanned (93%). Failure to obtain images were typical due to a sequential curved graft with anastomoses that could not be contained in the straight cavity of the stabilizing device, echo artefacts from a Titanium clip located in the roof of the anastomoses, and challenges in interpreting the images during the learning curve. No complications were associated with the ECUS procedure. The combined ECUS and TTFM resulted in immediate revision of five peripheral anastomoses. CONCLUSIONS: Peroperative use of a stabilizing device during ultrasonography of coronary artery bypass anastomoses in on-pump surgery facilitates imaging and provides surgeons with non-deformed longitudinal and transverse images of all parts of the anastomoses in all coronary territories. Peroperative ECUS in addition to flow measurements has the potential to increase the likelihood of detecting technical errors in constructed anastomoses. TRIAL REGISTRATION: The study was registered on September 29, 2016, ClinicalTrials.gov ID: NCT02919124.

Pancreatic magnetic resonance imaging texture analysis in chronic pancreatitis: a feasibility and validation study.

PURPOSE: This feasibility and validation study addresses the potential use of magnetic resonance imaging (MRI) texture analysis of the pancreas in patients with chronic pancreatitis (CP). METHODS: Extraction of 851 MRI texture features from diffusion weighted imaging (DWI) of the pancreas was performed in 77 CP patients and 22 healthy controls. Features were reduced to classify patients into subgroups, and a Bayes classifier was trained using a tenfold cross-validation forward selection procedure. The classifier was optimized to obtain the best average m-fold accuracy, sensitivity, specificity, and positive predictive value. Classifiers were: presence of disease (CP vs. healthy controls), etiological risk factors (alcoholic vs. nonalcoholic etiology of CP and tobacco use vs. no tobacco use), and complications to CP (presumed pancreatogenic diabetes vs. no diabetes and pancreatic exocrine insufficiency vs. normal pancreatic function). RESULTS: The best classification performance was obtained for the disease classifier selecting only five of the original features with 98% accuracy, 97% sensitivity, 100% specificity, and 100% positive predictive value. The risk factor classifiers obtained good performance using 9 (alcohol: 88% accuracy) and 10 features (tobacco: 86% accuracy). The two complication classifiers obtained similar accuracies with only 4 (diabetes: 83% accuracy) and 3 features (exocrine pancreatic function: 82% accuracy). CONCLUSION: Pancreatic texture analysis demonstrated to be feasible in patients with CP and discriminate clinically relevant subgroups based on etiological risk factors and complications. In future studies, the method may provide useful information on disease progression (monitoring) and detection of biomarkers characterizing early-stage CP.

A case report on epicardial ultrasonography of coronary anastomoses using a stabilizing device without the use of ultrasound gel.

BACKGROUND: Intraoperative epicardial ultrasonography of coronary artery bypass graft anastomoses is a procedure used for anatomical quality assessment of peripheral anastomoses during coronary artery bypass grafting. However, it may be difficult to keep the ultrasound transducer in steady contact with the anastomoses on the beating heart without causing any deformation. Furthermore, we are not aware of any sterile ultrasound gel approved for application into the pericardial space. CASE PRESENTATION: We report a method using a stabilizing connecting device for an ultrasound transducer to be used for visualization of coronary anastomoses without application of ultrasound gel during on-pump coronary bypass surgery. CONCLUSION: Use of a stabilizing device and coagulated blood from the patient as an alternative for ultrasound gel facilitates peroperative ultrasonography of coronary anastomoses. The procedure provides surgeons with non-deformed echocardiographic longitudinal and transverse images of all parts of the anastomoses. TRIAL REGISTRATION: The patient participated in a still ongoing clinical feasibility study: Trial registration: ClinicalTrials.gov ID: NCT02919124 ; Registered September 29, 2016.

Accurate Measurement of Airway Morphology on Chest CT Images.

In recent years, the ability to accurately measuring and analyzing the morphology of small pulmonary structures on chest CT images, such as airways, is becoming of great interest in the scientific community. As an example, in COPD the smaller conducting airways are the primary site of increased resistance in COPD, while small changes in airway segments can identify early stages of bronchiectasis. To date, different methods have been proposed to measure airway wall thickness and airway lumen, but traditional algorithms are often limited due to resolution and artifacts in the CT image. In this work, we propose a Convolutional Neural Regressor (CNR) to perform cross-sectional measurements of airways, considering wall thickness and airway lumen at once. To train the networks, we developed a generative synthetic model of airways that we refined using a Simulated and Unsupervised Generative Adversarial Network (SimGAN). We evaluated the proposed method by first computing the relative error on a dataset of synthetic images refined with SimGAN, in comparison with other methods. Then, due to the high complexity to create an in-vivo ground-truth, we performed a validation on an airway phantom constructed to have airways of different sizes. Finally, we carried out an indirect validation analyzing the correlation between the percentage of the predicted forced expiratory volume in one second (FEV1%) and the value of the Pi10 parameter. As shown by the results, the proposed approach paves the way for the use of CNNs to precisely and accurately measure small lung airways with high accuracy.

An Improved Algorithm for Coronary Bypass Anastomosis Segmentation in Epicardial Ultrasound Sequences.

Epicardial ultrasound (EUS) can be used for intra-operative quality assessment of coronary artery bypass anastomoses. To quantify the anastomotic quality from EUS images, the area of anastomotic structures has to be extracted from EUS sequences. Currently, this is done manually as no objective methods are available. We used an automatic anastomosis segmentation algorithm to extract the area of anastomotic structures from in vivo EUS sequences obtained from 16 porcine anastomoses. The algorithm consists of four major components: vessel detection, vessel segmentation, segmentation quality control and inter-frame contour alignment. The segmentation accuracy was assessed using m-fold cross-validation based on 830 manual segmentations of the anastomotic structures. A Dice coefficient of 0.879 (±0.073) and an absolute area difference of 16.95% (±17.94) were obtained. The proposed segmentation algorithm has potential to automatically extract the area of anastomotic structures.

Automatic detection of coronary artery anastomoses in epicardial ultrasound images.

PURPOSE: Epicardial ultrasound (EUS) can be used to assess the quality of coronary artery bypass graft surgery (CABG) anastomoses by determining stenotic rates. Currently, no objective quantitative methods are available for the analysis of EUS images. Therefore, surgeons have to be trained in interpreting EUS images, which may limit the use of EUS in clinical practice. Automatic detection of vessel structures can enable the objective and quantitative quality assessment of anastomoses without user interaction to facilitate the revision of anastomoses during the primary surgery. METHODS: An automatic vessel detection algorithm extracted and detected image regions that uniquely intersected with the vessel lumen of anastomotic structures. First, an initial pixel-based segmentation was performed from regional minimums using a watershed segmentation and an adaptive thresholding approach. A region-based merging step was then performed to merge oversegmented vessel structures using a Bayesian classification of different region combinations constructed from the pixel-based segmentations. Finally, a vessel classification step was performed on the extracted regions after the region-based merging to determine the probabilities that the regions contained vessel structures. RESULTS: The performance of the vessel classifier was tested using m-fold cross-validation of 320 EUS images containing anastomotic vessel structures from 16 anastomoses made on healthy porcine vessels. An area under the curve of 0.966 (95 % CI 0.951-0.984) and 0.989 (95 % CI 0.985-0.993, p < 0.001) of a precision-recall and receiver operator characteristic curve, respectively, was obtained when detecting vessel regions extracted from the EUS images. CONCLUSIONS: The vessel detection algorithm can detect vessel regions in EUS images at a high accuracy. It can be used to enable the automatic analysis of EUS images for the quality assessment of CABG anastomoses.

Semi-automatic vessel tracking and segmentation using epicardial ultrasound in bypass surgery.

The purpose of intraoperative quality assessment of coronary artery bypass graft surgery is to confirm graft patency and disclose technical errors to reduce cardiac mortality, morbidity and improve clinical outcome for the patient. Epicardial ultrasound has been suggested as an alternative approach for quality assessment of anastomoses. To make a quantitative assessment of the anastomotic quality using ultrasound images, the vessel border has to be delineated to estimate the area of the vessel lumen. A tracking and segmentation algorithm was developed consisting of an active contour modeling approach and quality control of the segmentations. Evaluation of the tracking algorithm showed 91.96% of the segmentations were segmented correct with a mean error in height and width at 5.65% and 11.50% respectively.