scholarly journals PathFlow-MixMatch for Whole Slide Image Registration: An Investigation of a Segment-Based Scalable Image Registration Method

2020 ◽  
Author(s):  
Joshua J. Levy ◽  
Christopher R. Jackson ◽  
Christian C. Haudenschild ◽  
Brock C. Christensen ◽  
Louis J. Vaickus
Keyword(s):  
Image Registration ◽  
Large Scale ◽  
Digital Pathology ◽  
Superior Performance ◽  
Registration Method ◽  
Target Registration Error ◽  
Alignment Algorithms ◽  
Slide Image ◽  
Segment Matching ◽  
The Impact

AbstractImage registration involves finding the best alignment between different images of the same object. In these tasks, the object in question is viewed differently in each of the images (e.g. different rotation or light conditions, etc.). In digital pathology, image registration aligns correspondent regions of tissue from different stereotactic viewpoints (e.g. subsequent deeper sections of the same tissue). These comparisons are important for histological analysis and can facilitate previously unavailable manipulations, such as 3D tissue reconstruction and cell-level alignment of immunohistochemical (IHC) and special stains. Several benchmarks have been established for evaluating image registration techniques for histological tissue; however, little work has evaluated the impact of scaling registration techniques to Giga-Pixel Whole Slide Images (WSI), which are large enough for significant memory limitations, and contain recurrent patterns and deformations that hinder traditional alignment algorithms. Furthermore, as tissue sections often contain multiple, discrete, smaller tissue fragments, it is unnecessary to align an entire image when the bulk of the image is background whitespace and tissue fragments’ orientations are often agnostic of each other. We present a methodology for circumventing large-scale image registration issues in histopathology and accompanying software. By removing background pixels, parsing the slide into discrete tissue segments, and matching, orienting and registering smaller segment pairs, we recovered registrations with lower Target Registration Error (TRE) when compared to utilizing the unmanipulated WSI. We tested our technique by having a pathologist annotate landmarks from 13 pairs of differently stained liver biopsy slides, performing WSI and segment-based registration techniques, and comparing overall TRE. Preliminary results demonstrate superior performance of registering segment pairs versus registering WSI (difference of median TRE of 44 pixels, p<0.001). Segment matching within WSI is an effective solution for histology image registration but requires further testing and validation to ensure its viability for stain translation and 3D histology analysis.

scholarly journals Damage detection using in-domain and cross-domain transfer learning

2021 ◽  
Author(s):  
Zaharah A. Bukhsh ◽  
Nils Jansen ◽  
Aaqib Saeed
Keyword(s):  
Damage Detection ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Large Scale ◽  
Visual Inspection ◽  
Superior Performance ◽  
Cross Domain ◽  
Domain Transfer ◽  
Domain Transfer Learning ◽  
The Impact

AbstractWe investigate the capabilities of transfer learning in the area of structural health monitoring. In particular, we are interested in damage detection for concrete structures. Typical image datasets for such problems are relatively small, calling for the transfer of learned representation from a related large-scale dataset. Past efforts of damage detection using images have mainly considered cross-domain transfer learning approaches using pre-trained ImageNet models that are subsequently fine-tuned for the target task. However, there are rising concerns about the generalizability of ImageNet representations for specific target domains, such as for visual inspection and medical imaging. We, therefore, evaluate a combination of in-domain and cross-domain transfer learning strategies for damage detection in bridges. We perform comprehensive comparisons to study the impact of cross-domain and in-domain transfer, with various initialization strategies, using six publicly available visual inspection datasets. The pre-trained models are also evaluated for their ability to cope with the extremely low-data regime. We show that the combination of cross-domain and in-domain transfer persistently shows superior performance specially with tiny datasets. Likewise, we also provide visual explanations of predictive models to enable algorithmic transparency and provide insights to experts about the intrinsic decision logic of typically black-box deep models.

scholarly journals Digital Pathology: Advantages, Limitations and Emerging Perspectives

2020 ◽  
Vol 9 (11) ◽  
pp. 3697
Author(s):  
Stephan W. Jahn ◽  
Markus Plass ◽  
Farid Moinfar
Keyword(s):  
Artificial Intelligence ◽  
Occupational Health ◽  
Large Scale ◽  
Digital Pathology ◽  
Health Issues ◽  
Routine Diagnostics ◽  
Starting Point ◽  
Slide Image ◽  
Traditional Work ◽  
Image Archiving

Digital pathology is on the verge of becoming a mainstream option for routine diagnostics. Faster whole slide image scanning has paved the way for this development, but implementation on a large scale is challenging on technical, logistical, and financial levels. Comparative studies have published reassuring data on safety and feasibility, but implementation experiences highlight the need for training and the knowledge of pitfalls. Up to half of the pathologists are reluctant to sign out reports on only digital slides and are concerned about reporting without the tool that has represented their profession since its beginning. Guidelines by international pathology organizations aim to safeguard histology in the digital realm, from image acquisition over the setup of work-stations to long-term image archiving, but must be considered a starting point only. Cost-efficiency analyses and occupational health issues need to be addressed comprehensively. Image analysis is blended into the traditional work-flow, and the approval of artificial intelligence for routine diagnostics starts to challenge human evaluation as the gold standard. Here we discuss experiences from past digital pathology implementations, future possibilities through the addition of artificial intelligence, technical and occupational health challenges, and possible changes to the pathologist’s profession.

Navigating COVID-19 with emotional intelligence

2020 ◽  
Vol 66 (8) ◽  
pp. 810-820
Author(s):  
Mubashir Majid Baba
Keyword(s):  
Emotional Intelligence ◽  
Large Scale ◽  
Demographic Variable ◽  
Total Sample ◽  
Demographic Variables ◽  
Faculty Members ◽  
Superior Performance ◽  
Primary Data ◽  
Secondary Sources ◽  
The Impact

Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a newly discovered coronavirus. COVID-19 has affected educational systems worldwide, leading to the widespread closure of schools, colleges and universities. The COVID-19 pandemic is also having a dramatic impact on societies and economies around the world. With various measures of lockdowns and social distancing in place, it becomes important to understand emotional intelligence of faculty members working in institutions of higher learning on a large scale in this pandemic. Aim: The purpose of this article is to examine the perception of faculty members toward their emotional intelligence during COVID-19 and to study the impact of demographic variables on their emotional intelligence. Method: The data collected were analyzed using descriptive and inferential statistics. The data for the study were collected through both the primary and secondary sources. Online questionnaires were used to gather the primary data. The measuring items used for the study were sourced from existing validated scales and literature. Descriptive statistics was employed to know the descriptive information across various demographic variables on a total sample of 683. The various demographic variables, which were considered for the study, were gender and designation. Results: The results revealed that the faculty members perceived their emotional intelligence at an above-average level in the present pandemic, that is, COVID-19. The results also revealed that the perception of the respondent faculty members toward their emotional intelligence from different universities and states is more or less the same and also the demographic variable gender has a significant impact on emotional intelligence during the present pandemic. Conclusion: Besides having theoretical implications that open pathways for conducting further research, the findings of the study may serve as a reference for service practitioners in designing strategies that will ensure superior performance of faculty members in higher educational institutions during the pandemic.

An Abdominal Registration Technology for Integration of Nanomaterial Imaging-Aided Diagnosis and Treatment

2021 ◽  
Vol 17 (5) ◽  
pp. 952-959
Author(s):  
Shao-Di Yang ◽  
Yu-Qian Zhao ◽  
Fan Zhang ◽  
Miao Liao ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Image Registration ◽  
Unsupervised Learning ◽  
Large Scale ◽  
Three Dimensional ◽  
Initial Step ◽  
Abdominal Ct ◽  
Registration Method ◽  
Therapy Effect ◽  
Coarse To Fine ◽  
Aided Diagnosis

Image registration technology is a key technology used in the process of nanomaterial imaging-aided diagnosis and targeted therapy effect monitoring for abdominal diseases. Recently, the deep-learning based methods have been increasingly used for large-scale medical image registration, because their iteration is much less than those of traditional ones. In this paper, a coarse-to-fine unsupervised learning-based three-dimensional (3D) abdominal CT image registration method is presented. Firstly, an affine transformation was used as an initial step to deal with large deformation between two images. Secondly, an unsupervised total loss function containing similarity, smoothness, and topology preservation measures was proposed to achieve better registration performances during convolutional neural network (CNN) training and testing. The experimental results demonstrated that the proposed method severally obtains the average MSE, PSNR, and SSIM values of 0.0055, 22.7950, and 0.8241, which outperformed some existing traditional and unsupervised learning-based methods. Moreover, our method can register 3D abdominal CT images with shortest time and is expected to become a real-time method for clinical application.

Automatic Retinal Image Registration Using Blood Vessel Segmentation and SIFT Feature

2017 ◽  
Vol 31 (11) ◽  
pp. 1757006 ◽  
Author(s):  
Fan Guo ◽  
Xin Zhao ◽  
Beiji Zou ◽  
Yixiong Liang
Keyword(s):  
Image Registration ◽  
Blood Vessel ◽  
Retinal Image ◽  
Vessel Segmentation ◽  
Experimental Results ◽  
Superior Performance ◽  
Registration Method ◽  
Sift Feature ◽  
Blood Vessel Segmentation ◽  
Segmentation Image

Automatic retinal image registration is still a great challenge in computer aided diagnosis and screening system. In this paper, a new retinal image registration method is proposed based on the combination of blood vessel segmentation and scale invariant feature transform (SIFT) feature. The algorithm includes two stages: retinal image segmentation and registration. In the segmentation stage, the blood vessel is segmented by using the guided filter to enhance the vessel structure and the bottom-hat transformation to extract blood vessel. In the registration stage, the SIFT algorithm is adopted to detect the feature of vessel segmentation image, complemented by using a random sample consensus (RANSAC) algorithm to eliminate incorrect matches. We evaluate our method from both segmentation and registration aspects. For segmentation evaluation, we test our method on DRIVE database, which provides manually labeled images from two specialists. The experimental results show that our method achieves 0.9562 in accuracy (Acc), which presents competitive performance compare to other existing segmentation methods. For registration evaluation, we test our method on STARE database, and the experimental results demonstrate the superior performance of the proposed method, which makes the algorithm a suitable tool for automated retinal image analysis.

scholarly journals Joint Attention Neural Model for Demand Prediction in Online Marketplaces

2020 ◽  
Vol 1 ◽  
pp. 6
Author(s):  
Ashish Gupta ◽  
Rishabh Mehrotra
Keyword(s):  
Inventory Management ◽  
Large Scale ◽  
Contextual Information ◽  
Neural Model ◽  
Superior Performance ◽  
Real World Data ◽  
Demand Prediction ◽  
Online Marketplaces ◽  
Textual Content ◽  
The Impact

As an increasing number of consumers rely on online marketplaces to purchase goods from, demand prediction becomes an important problem for suppliers to inform their pricing and inventory management decisions. Business volatility and the complexity of factors influence demand, which makes it a harder quantity to predict. In this paper, we consider the case of an online classified marketplace and propose a joint multi-modal neural model for demand prediction. The proposed neural model incorporates a number of factors including product description information (title, description, images), contextual information (geography, similar products) and historic interest to predict demand. Large-scale experiments on real-world data demonstrate superior performance over established baselines. Our experiments highlight the importance of considering, quantifying and leveraging the textual content of products and image quality for enhanced demand prediction. Finally, we quantify the impact of the different factors in predicting demand.

scholarly journals Robust Hierarchical Density Estimation and Regression for Re-stained Histological Whole Slide Image Co-registration

2019 ◽  
Author(s):  
Jun Jiang ◽  
Nicholas B. Larson ◽  
Naresh Prodduturi ◽  
Thomas J. Flotte ◽  
Steven N. Hart
Keyword(s):  
Image Registration ◽  
Density Estimation ◽  
Digital Pathology ◽  
Tissue Samples ◽  
Registration Errors ◽  
Slide Image ◽  
Specific Proteins ◽  
Histological Images ◽  
Hematoxylin And Eosin ◽  
Whole Slide Images

AbstractFor many disease conditions, tissue samples are colored with multiple dyes and stains to add contrast and location information for specific proteins to accurately identify and diagnose disease. This presents a computational challenge for digital pathology, as whole-slide images (WSIs) need to be properly overlaid (i.e. registered) to identify co-localized features. Traditional image registration methods sometimes fail due to the high variation of cell density and insufficient texture information in WSIs – particularly at high magnifications. In this paper, we proposed a robust image registration strategy to align re-stained WSIs precisely and efficiently. This method is applied to 30 pairs of immunohistochemical (IHC) stains and their hematoxylin and eosin (H&E) counterparts. Our approach advances the existing methods in three key ways. First, we introduce refinements to existing image registration methods. Second, we present an effective weighting strategy using kernel density estimation to mitigate registration errors. Third, we account for the linear relationship across WSI levels to improve accuracy. Our experiments show significant decreases in registration errors when on matching IHC and H&E pairs, enabling subcellular-level analysis on stained and re-stained histological images. We also provide a tool to allow users to develop their own registration benchmarking experiments.

scholarly journals Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4085
Author(s):  
Marek Wodzinski ◽  
Izabela Ciepiela ◽  
Tomasz Kuszewski ◽  
Piotr Kedzierawski ◽  
Andrzej Skalski
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Real Time ◽  
Breast Tumor ◽  
Tumor Resection ◽  
Registration Method ◽  
Registration Error ◽  
Target Registration Error ◽  
Tumor Bed ◽  
Registration Process

Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiated is not trivial. The automatic image registration could significantly aid the tumor bed localization and lower the radiation dose delivered to the surrounding healthy tissues. This study proposes a novel image registration method dedicated to breast tumor bed localization addressing the problem of missing data due to tumor resection that may be applied to real-time radiotherapy planning. We propose a deep learning-based nonrigid image registration method based on a modified U-Net architecture. The algorithm works simultaneously on several image resolutions to handle large deformations. Moreover, we propose a dedicated volume penalty that introduces the medical knowledge about tumor resection into the registration process. The proposed method may be useful for improving real-time radiation therapy planning after the tumor resection and, thus, lower the surrounding healthy tissues’ irradiation. The data used in this study consist of 30 computed tomography scans acquired in patients with diagnosed breast cancer, before and after tumor surgery. The method is evaluated using the target registration error between manually annotated landmarks, the ratio of tumor volume, and the subjective visual assessment. We compare the proposed method to several other approaches and show that both the multilevel approach and the volume regularization improve the registration results. The mean target registration error is below 6.5 mm, and the relative volume ratio is close to zero. The registration time below 1 s enables the real-time processing. These results show improvements compared to the classical, iterative methods or other learning-based approaches that do not introduce the knowledge about tumor resection into the registration process. In future research, we plan to propose a method dedicated to automatic localization of missing regions that may be used to automatically segment tumors in the source image and scars in the target image.

Impact of COVID-19 on Nuclear Medicine in Germany, Austria and Switzerland: An International Survey in April 2020

2020 ◽  
Vol 59 (04) ◽  
pp. 294-299 ◽  
Author(s):  
Lutz S. Freudenberg ◽  
Ulf Dittmer ◽  
Ken Herrmann
Keyword(s):  
Nuclear Medicine ◽  
Health Systems ◽  
Personal Protective Equipment ◽  
Large Scale ◽  
Diagnostic Procedures ◽  
Protective Equipment ◽  
Current Crisis ◽  
Web Based ◽  
Public Versus Private ◽  
The Impact

Abstract Introduction Preparations of health systems to accommodate large number of severely ill COVID-19 patients in March/April 2020 has a significant impact on nuclear medicine departments. Materials and Methods A web-based questionnaire was designed to differentiate the impact of the pandemic on inpatient and outpatient nuclear medicine operations and on public versus private health systems, respectively. Questions were addressing the following issues: impact on nuclear medicine diagnostics and therapy, use of recommendations, personal protective equipment, and organizational adaptations. The survey was available for 6 days and closed on April 20, 2020. Results 113 complete responses were recorded. Nearly all participants (97 %) report a decline of nuclear medicine diagnostic procedures. The mean reduction in the last three weeks for PET/CT, scintigraphies of bone, myocardium, lung thyroid, sentinel lymph-node are –14.4 %, –47.2 %, –47.5 %, –40.7 %, –58.4 %, and –25.2 % respectively. Furthermore, 76 % of the participants report a reduction in therapies especially for benign thyroid disease (-41.8 %) and radiosynoviorthesis (–53.8 %) while tumor therapies remained mainly stable. 48 % of the participants report a shortage of personal protective equipment. Conclusions Nuclear medicine services are notably reduced 3 weeks after the SARS-CoV-2 pandemic reached Germany, Austria and Switzerland on a large scale. We must be aware that the current crisis will also have a significant economic impact on the healthcare system. As the survey cannot adapt to daily dynamic changes in priorities, it serves as a first snapshot requiring follow-up studies and comparisons with other countries and regions.

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