Building Blocks for Integrating Image Analysis Algorithms into a Clinical Workflow

PurposeStarting from a broad-based needs assessment and utilizing an image analysis algorithm (IAA) developed at our institution, the purpose of this study was to define generalizable building blocks necessary for the integration of any IAA into a clinical practice.MethodsAn IAA was developed in our institution to process lymphoscintigraphy exams. A team of radiologists defined a set of building blocks for integration of this IAA into clinical workflow. The building blocks served the following roles: (1) Timely delivery of images to the IAA, (2) quality control, (3) IAA results processing, (4) results presentation & delivery, (5) IAA error correction, (6) system performance monitoring, and (7) active learning. Utilizing these modules, the lymphoscintigraphy IAA was integrated into the clinical workflow at our institution. System performance was tested over a 1 month period, including assessment of number of exams processed and delivered, and error rates and corrections.ResultsFrom June 26-July 27, 2019, the building blocks were used to integrate IAA results from 132 lymphoscintigraphy exams into the clinical workflow, representing 100% of the exams performed during the time period. The system enabled radiologists to correct 21 of the IAA results. All results and corrections were successfully stored in a database. A dashboard allowed the development team to monitor system performance in real-time.ConclusionsWe describe seven building blocks that optimize the integration of IAAs into clinical workflow. The implementation of these building blocks in this study can be used to inform development of more robust, standards-based solutions.

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A dynamic network simulator for testing TP monitor system performance and behavior

10.1109/iccas.2007.4406647 ◽

2007 ◽

Author(s):

Maria Luisa Catalan ◽

Dennis Ludena ◽

Hidenori Umeno

Keyword(s):

System Performance ◽

Dynamic Network ◽

Monitor System ◽

Network Simulator ◽

And Behavior

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A predictive and parametrized architecture for image analysis algorithm implementations on FPGA adapted to multispectral imaging

2008 First Workshops on Image Processing Theory, Tools and Applications ◽

10.1109/ipta.2008.4743765 ◽

2008 ◽

Cited By ~ 1

Author(s):

Junyan Tan ◽

Linlin Zhang ◽

Virginie Fresse ◽

Anne Claire Legrand ◽

Dominique Houzet

Keyword(s):

Image Analysis ◽

Multispectral Imaging ◽

Analysis Algorithm ◽

Image Analysis Algorithm

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CONTROL SYSTEM PERFORMANCE MONITORING: NEW DEVELOPMENTS AND PRACTICAL ISSUES

IFAC Proceedings Volumes ◽

10.3182/20020721-6-es-1901.01618 ◽

2002 ◽

Vol 35 (1) ◽

pp. 387-392 ◽

Cited By ~ 5

Author(s):

G.A. Dumont ◽

L. Kammer ◽

B.J. Allison ◽

L. Ettaleb ◽

A.A. Roche

Keyword(s):

Control System ◽

System Performance ◽

Performance Monitoring ◽

New Developments ◽

System Performance Monitoring

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Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

Journal of Imaging ◽

10.3390/jimaging7020019 ◽

2021 ◽

Vol 7 (2) ◽

pp. 19

Author(s):

Tirivangani Magadza ◽

Serestina Viriri

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Brain Tumor ◽

Quantitative Analysis ◽

Medical Image ◽

State Of The Art ◽

Medical Image Analysis ◽

Building Blocks ◽

Tumor Segmentation ◽

Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

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Analysis algorithm for sky type and ice halo recognition in all-sky images

Atmospheric Measurement Techniques ◽

10.5194/amt-12-4241-2019 ◽

2019 ◽

Vol 12 (8) ◽

pp. 4241-4259 ◽

Cited By ~ 1

Author(s):

Sylke Boyd ◽

Stephen Sorenson ◽

Shelby Richard ◽

Michelle King ◽

Morton Greenslit

Keyword(s):

Time Series ◽

Image Analysis ◽

Great Plains ◽

Detailed Comparison ◽

Analysis Algorithm ◽

Long Time Series ◽

Image Analysis Algorithm ◽

Long Time ◽

Atmospheric Radiation Measurement ◽

Sky Type

Abstract. Halo displays, in particular the 22∘ halo, have been captured in long time series of images obtained from total sky imagers (TSIs) at various Atmospheric Radiation Measurement (ARM) sites. Halo displays form if smooth-faced hexagonal ice crystals are present in the optical path. We describe an image analysis algorithm for long time series of TSI images which scores images with respect to the presence of 22∘ halos. Each image is assigned an ice halo score (IHS) for 22∘ halos, as well as a photographic sky type (PST), which differentiates cirrostratus (PST-CS), partially cloudy (PST-PCL), cloudy (PST-CLD), or clear (PST-CLR) within a near-solar image analysis area. The color-resolved radial brightness behavior of the near-solar region is used to define the discriminant properties used to classify photographic sky type and assign an ice halo score. The scoring is based on the tools of multivariate Gaussian analysis applied to a standardized sun-centered image produced from the raw TSI image, following a series of calibrations, rotation, and coordinate transformation. The algorithm is trained based on a training set for each class of images. We present test results on halo observations and photographic sky type for the first 4 months of the year 2018, for TSI images obtained at the Southern Great Plains (SGP) ARM site. A detailed comparison of visual and algorithm scores for the month of March 2018 shows that the algorithm is about 90 % reliable in discriminating the four photographic sky types and identifies 86 % of all visual halos correctly. Numerous instances of halo appearances were identified for the period January through April 2018, with persistence times between 5 and 220 min. Varying by month, we found that between 9 % and 22 % of cirrostratus skies exhibited a full or partial 22∘ halo.

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Osteoid Metaplasia in Femoral Artery Plaques Is Associated With the Clinical Severity of Lower Extremity Artery Disease in Men

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2020.594192 ◽

2020 ◽

Vol 7 ◽

Author(s):

Mirjami Laivuori ◽

Johanna Tolva ◽

A. Inkeri Lokki ◽

Nina Linder ◽

Johan Lundin ◽

...

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Lower Extremity ◽

Femoral Artery ◽

Analysis Algorithm ◽

Image Analysis Algorithm ◽

Artery Disease ◽

Area Percentage ◽

Lower Extremity Artery Disease ◽

Whole Slide Images

Lamellar metaplastic bone, osteoid metaplasia (OM), is found in atherosclerotic plaques, especially in the femoral arteries. In the carotid arteries, OM has been documented to be associated with plaque stability. This study investigated the clinical impact of OM load in femoral artery plaques of patients with lower extremity artery disease (LEAD) by using a deep learning-based image analysis algorithm. Plaques from 90 patients undergoing endarterectomy of the common femoral artery were collected and analyzed. After decalcification and fixation, 4-μm-thick longitudinal sections were stained with hematoxylin and eosin, digitized, and uploaded as whole-slide images on a cloud-based platform. A deep learning-based image analysis algorithm was trained to analyze the area percentage of OM in whole-slide images. Clinical data were extracted from electronic patient records, and the association with OM was analyzed. Fifty-one (56.7%) sections had OM. Females with diabetes had a higher area percentage of OM than females without diabetes. In male patients, the area percentage of OM inversely correlated with toe pressure and was significantly associated with severe symptoms of LEAD including rest pain, ulcer, or gangrene. According to our results, OM is a typical feature of femoral artery plaques and can be quantified using a deep learning-based image analysis method. The association of OM load with clinical features of LEAD appears to differ between male and female patients, highlighting the need for a gender-specific approach in the study of the mechanisms of atherosclerotic disease. In addition, the role of plaque characteristics in the treatment of atherosclerotic lesions warrants further consideration in the future.

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Power Neighboring Interval Matching Based PMC Integration

Journal of Circuits System and Computers ◽

10.1142/s0218126616500869 ◽

2016 ◽

Vol 25 (08) ◽

pp. 1650086

Author(s):

Yuelong Li ◽

Jigang Wu ◽

Yawen Chen ◽

Jason Mair ◽

David Eyers ◽

...

Keyword(s):

Data Integration ◽

Power Consumption ◽

Power Dissipation ◽

System Performance ◽

Performance Monitoring ◽

Power Estimation ◽

Raw Data ◽

Integration Approach ◽

Common Task ◽

The Status

Performance monitoring counters (PMCs) are of great value to monitor the status of processors and their further analysis and modeling. In this paper, we explore a novel problem called PMC integration, i.e., how to combine a group of PMCs which are collected asynchronously together. It is well known that, due to hardware constraints, the number of PMCs that can be measured concurrently is strictly limited. It means we cannot directly acquire all the phenomenon features that are related with the system performance. Clearly, this source raw data shortage is extremely frustrating to PMCs based analysis and modeling tasks, such as PMCs based power estimation. To deal with this problem, we introduce a neighboring interval power values based PMC data integration approach. Based on the activity similarity of easily collected power dissipation values, the proposed approach can automatically combine distinct categories of PMC data together and hence realize the recovery of intact raw PMC data. In addition, the significance and effectiveness of the proposed approach are experimentally verified on a common task, the PMCs based power consumption modeling.

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