intel oneAPI Base Toolkit Helps SonoScape Optimize the Performance of its S-Fetus 4.0 Obstetric Screening Assistant User Guide
- June 3, 2024
- Intel
Table of Contents
Solution Brief
Health & Life Sciences
oneAPI Base Toolkit Helps SonoScape
Optimize the Performance of its S-Fetus 4.0
Obstetric Screening Assistant
User Guide
oneAPI Base Toolkit Helps SonoScape Optimize the Performance of its
S-Fetus 4.0 Obstetric Screening Assistant
“With our commitment to the independent R&D and innovation of medical
equipment, SonoScape is pleased to state that our cutting-edge AI technology,
powered by Intel® oneAPI architecture, has been able to realize its potential
to serve medical institutions around the world.”
Feng Naizhang
Vice President, SonoScape
Obstetric screening is key to reducing maternal and perinatal mortality;
however, conventional obstetric screening methods require high levels of
medical expertise and are both time- and labor-intensive. To address these
issues, SonoScape has launched a smart obstetric screening system based on
artificial intelligence (AI) and other technologies. The system automates
output of screening results through automatic structure recognition,
measurement, classification, and diagnosis to significantly enhance efficiency
and reduce the workload of doctors.¹
The S-Fetus 4.0 Obstetric Screening Assistant 2 uses deep learning to power a
smart scenario-based work model that allows doctors to perform sonography
without the need to manually control equipment and enables real-time dynamic
acquisition of standard planes and automatic measurement of fetal biometry and
growth index, an industry first. Sonoscape’s aim is to simplify obstetric
screening workflows and make it easier for patients to get care. To enhance
its performance, SonoScape used the Intel® oneAPI Base Toolkit for cross-
architecture development and optimization to speed processing of multimodal
data. Through a platform based on the Intel® Core™ i7 processor, performance
was increased by approximately 20x 3 while achieving higher price performance,
cross-architecture scalability, and flexibility.
Background: Applications and Challenges of Diagnostic Ultrasound in
Obstetric Examinations
Diagnostic ultrasound is a technique in which ultrasound is used to measure
the data and morphology of a patient’s physiology or tissue structure to
discover diseases and provide medical guidance. 4 Owing to safety, non-
invasiveness, cost performance, practicality, repeatability, and broad
adaptability, the market for diagnostic ultrasound equipment is growing
rapidly. According to data from Fortune Business Insights, the size of the
global diagnostic ultrasound equipment market was USD 7.26 billion in 2020,
and is expected to reach USD 12.93 billion by the end of 2028, representing a
compound annual growth rate (CAGR) of 7.8%. 5
Though 2D ultrasound is indispensable for the diagnosis of obstetric and
gynecological diseases (especially in intrauterine fetal testing),
conventional ultrasonography techniques rely heavily on the expertise of the
sonographer. As time-consuming and skill-intensive manual operations are
required throughout the entire process, ultrasonography poses challenges to
hospitals in smaller communities and less-developed areas that have limited
access to medical technology.
To address these issues, SonoScape has developed a smart diagnostic ultrasound
solution based on AI technologies that are capable of classification,
detection, and segmentation of a variety of anatomical structures from
ultrasound images through deep learning algorithms represented by
convolutional neural networks (CNNs). 6 However, the current diagnostic
ultrasound solution faces several challenges:
- The equipment requires a high amount of user intervention and possesses inherent delays, such as when the operator must adapt to different operating procedures when switching between modes.
- Computing power requirements are rising as AI algorithms grow in complexity. These algorithms often use external accelerators, such as GPUs, that increase cost, use more power, and require additional testing and certification. Continuous AI optimization for the best performance and user experience has become a key challenge.
SonoScape Utilizes the Intel oneAPI Base Toolkit to Optimize the
Performance of its S-Fetus 4.0 Obstetric Screening Assistant
SonoScape S-Fetus 4.0 Obstetric Screening Assistant
Based on the standardized collection and measurement of ultrasound scan
sections, clinicians can use obstetric screening to detect most fetal
structural abnormalities. SonoScape’s proprietary S-Fetus 4.0 Obstetric
Screening Assistant is the first globally available smart obstetric screening
technology based on deep learning. When combined with the SonoScape P60 and
S60 ultrasound platforms, the S-Fetus 4.0 is capable of real-time recognition
of sections during the sonography process, automatic acquisition of standard
sections, automatic measurement, and automatic feeding of results into the
corresponding fetal growth sections of the medical report. Boasting the first
smart obstetric screening function in the industry, the S-Fetus 4.0
significantly improves on conventional human-computer interaction methods by
providing a smart scenario-based work model that allows doctors to perform
sonography without the need to manually control complex equipment, simplifying
the sonogram process, improving efficiency, and reducing the workload of the
sonographer. The function provides effective frontend quality control during
the ultrasound process, enhances screening quality, and provides additional
guiding data in real time to help both doctors and patients.
Figure 1. SonoScape’s professional P60 obstetrics device equipped with
the S-Fetus 4.0
Utilizing core algorithms, original architecture, and cross-architecture hardware, the S-Fetus 4.0 achieves a fundamental technical breakthrough that provides a smart, scenario-based, full-process, and easily adoptable solution to improve the work efficiency and consistency of doctors. Comprehensive scenario-based functions ensure that doctors do not need to switch between manual and smart modes by default throughout the entire process, and reports can be completed with the swipe of a finger.
Figure 2. Process diagram of the S-Fetus 4.0 Obstetric Screening Assistant
The front end of the S-Fetus 4.0 generates multimodal data in accordance with
scenario requirements, while post processing handles reconstruction,
processing, and optimization. Working on reconstructed and optimized data, the
real-time AI recognition and tracking module analyzes and extracts standard
surfaces. In this process, the standard surface decision-making and dispatch
module follows a predefined strategy to adaptively extract quantified
features, then it performs quantitative analysis and automatically integrates
into subsequent operations.
During development, SonoScape and Intel engineers worked together to address
several challenges:
-
Further performance optimization. Many relevant deep learning algorithms must work in tandem to rapidly process tasks that use different data types and to optimally execute user-initiated tasks without latency. This results in higher computing power and algorithm optimization requirements for ultrasound platforms.
-
Mobile application demands. The SonoScape diagnostic ultrasound system with S-Fetus 4.0 Obstetric Screening Assistant is a mobile system with limits on overall power
consumption and system size, making it a challenge to use discrete GPUs. -
Cross-architecture expansion for different scenarios. The S-Fetus 4.0 Obstetric Screening Assistant needs to support migration and expansion across multiple architectures to operate in a variety of complex scenarios.
To solve these challenges, SonoScape partnered with Intel to optimize the AI performance of its obstetric screening assistant by using the Intel oneAPI Base Toolkit.
Intel oneAPI Toolkits
OneAPI is a cross-industry, open, standards-based unified programming model
that delivers a common developer experience across architectures for faster
application performance, more productivity, and greater innovation. The oneAPI
initiative encourages collaboration on the common specifications and
compatible oneAPI implementations across the ecosystem.
The model is designed to simplify the development process across multiple
architectures (such as CPUs, GPUs, FPGAs, and other accelerators). With a
complete set of crossarchitecture libraries and tools, oneAPI helps developers
develop performant code quickly and correctly across heterogeneous
environments.
As shown in Figure 3, the oneAPI project aims to build on Intel’s rich
heritage of CPU tools and expand to XPUs. It includes a complete set of
advanced compilers, libraries and porting, analysis, and debugging tools.
Intel’s reference implementation of oneAPI is a set of toolkits. Intel oneAPI
Base Toolkit for Native Code Developers is a core set of high-performance
tools for building C++, Data Parallel C++ applications, and oneAPI library-
based applications.
Application Workloads Need Diverse Hardware
Figure 3. Intel oneAPI Base Toolkit
Intel oneAPI Base Toolkit Helps SonoScape Optimize the Performance of its
Obstetric Screening Assistant
After integrating the Intel oneAPI Base Toolkit to their system, SonoScape
noted several paths to optimization.
At the hardware layer, the solution utilizes a computing architecture based on
an 11th Gen Intel® Core™ i7 processor that delivers enhanced execution
performance, eatures a new core and graphics architecture, and provides AI-
based optimization for excellent performance for various loads. Equipped with
Intel® Deep Learning Boost (Intel® DL Boost) technology, the processor
provides strong support for AI engines and enhanced performance for complex
loads such as AI and data analysis.
11th Gen Intel Core processors also have integrated Intel® Iris® Xe graphics,
enabling workloads to leverage this integrated GPU. It can support a rich
variety of data types and features a low-power architecture.
The data processing flow of the solution is shown below (Figure 4). Equipped
with cores optimized for the handling of data-intensive loads, the Intel Iris
Xe graphics are responsible for real-time recognition and tracking processes
and the realization of high-frequency real-time execution (each image frame
must be processed or intelligently inferred).
The Intel Core i7 processor handles standard surface decision-making and
dispatch; adaptive section feature extraction, quantitative analysis, and
other processes; and the execution of operational logic and AI inference
during downtime. Data-intensive and responsible for logical inference, the
multimodal data optimization and processing module has been optimized in five
key aspects through the oneAPI Toolkit. After optimization, the SonoScape
obstetric screening assistant can flexibly utilize all CPU and iGPU resources,
providing enhanced performance to meet operational demands and improve the
patient experience.
SonoScape and Intel focused on the optimization and performance testing of the
following platform:
Figure 4. Architecture of the SonoScape obstetric screening assistant
Comprehensive Performance Optimization using Intel Software Tools
Optimization #1: First, SonoScape used the Intel® VTune™ Profiler to analyze
their workload. The profiler can quickly identify CPU and GPU load performance
bottlenecks and provide relevant information. As shown in the figure below,
vector processing makes full use of Intel’s high instruction throughput and
supports the parallel processing of data to rapidly improve performance over
scalar operations.
Figure 5. Scalar processing vs. Vector processing
SonoScape also made use of the DPC++ Compiler in the oneAPI toolkit to
recompile its code and generate vector instructions for enhanced performance,
reducing the processing speed of the workload from 141 ms to just 33 ms⁷.
Optimization #2. Once performance bottlenecks were identified by the VTune
Profiler, SonoScape replaced them with APIs from Intel® Integrated Performance
Primitives
(Intel® IPP), a cross-platform software library of functions that include
accelerators for image processing, signal processing, data compression,
encryption mechanisms, and other applications. Intel IPP can be optimized for
CPUs to unlock the latest features of Intel architecture platforms (such as
AVX-512) to improve application performance.
For example, the ippsCrossCorrNorm_32f and ippsDotProd_32f64f functions can
improve performance by removing dual-layer loop calculations and
multiplication/ addition loops. Through such optimization, SonoScape was able
to further improve the processing speed of the workload from 33 ms to 13.787
ms⁷.
Optimization #3. Originally developed by Intel, the Open Source Computer
Vision Library (OpenCV) OpenCV can be used to develop real-time image
processing, computer vision, and pattern recognition programs, and supports
the utilization of Intel IPP for accelerated processing⁸.
By replacing OpenCV functions in the source code with IPP functions, the
solution scales well in large-scale data scenarios and performs well across
all generations of Intel platforms.
Optimization #4. Sonoscape’s S-Fetus 4.0 obstetric screening assistant also
utilizes the Intel® DPC++ Compatibility Tool to efficiently migrate existing
CUDA code to DPC++, ensuring cross-architecture compatibility and minimizing
the time required for migration. As shown in Figure 6, the tool provides
powerful interactive functions to help developers migrate CUDA code, including
kernel code and API calls. The tool can automatically migrate 80-90 percent⁹
of the code (depending on complexity) and embeds comments to help developers
complete the manual step of the migration process. In this case study, nearly
100 percent of the code was automatically migrated in a readable and usable
manner.
Figure 6. Workflow chart of the Intel DPC++ Compatibility Tool
After these optimizations were completed, the performance of the SonoScape S-Fetus 4.0 running on heterogeneous platform based on Intel oneAPI DPC++ was increased by nearly 20x that of the baseline performance data recorded before optimization, as shown in figure 7⁷.
Time Optimization of Multimodal Workload (ms lower is better) Figure 7. Performance Improvement with the Intel oneAPI Base Toolkit⁷
(Baseline: Code before optimization; Optimization 1: Intel oneAPI DPC++
Compiler; Optimization 2: Intel IPP used to replace loop source code;
Optimization 3: Intel IPP used to replace OpenCV functions; Optimization 4:
CPU + iGPU execution after CUDA migration)
Result: Excellent Performance and CrossArchitecture Scalability
By using Intel Core i7 processors with integrated Intel Iris Xe graphics to
provide underlying computing power and the Intel oneAPI heterogeneous platform
for optimization, the SonoScape obstetric screening assistant was able to
balance performance, cost-effectiveness, and scalability across multiple
platforms.
- Performance. By using Intel XPUs and Intel oneAPI Toolkits, the SonoScape obstetric screening assistant was able to realize up to 20x improved performance vs. unoptimized systems, laying a solid foundation for efficient obstetric diagnostic ultrasound⁷.
- Cost savings. By performing comprehensive optimization and using the powerful performance and flexible architecture of Intel Core i7 processor, SonoScape only needs CPU and iGPU resources to achieve its performance targets. These hardware simplifications reduce the demands for power supply, heat dissipation, and space. The solution can now be mounted on smaller diagnostic ultrasound equipment for more flexible configuration options. The integration of CPU and iGPU resources also provides longer battery life, along with higher scalability and reliability.
- Heterogeneous Scalability. The solution supports unified programming on heterogeneous hardware such as CPUs and iGPUs, improves the development efficiency of cross-architecture programming, and enables the flexible execution of obstetric screening assistants on different hardware configurations all while ensuring a smooth user
experience.
Outlook: Accelerated Integration of AI and Medical Applications
Smart diagnostic ultrasound is a key application of the integration of AI and
medical technologies that helps to reduce doctor workloads and improve the
speed of medical processes¹⁰. To facilitate the use of AI and medical
applications, Intel is working with partners such as SonoScape to accelerate
digital innovation through XPU architecture made up of CPUs, iGPUs, dedicated
accelerators, FPGAs, and software and hardware products such as the oneAPI
programming model in the medical industry.
“The Intel® oneAPI Base Toolkit helped us optimize key modules in an efficient
manner, realizing a 20x⁷ increase in performance and unified development on
cross-architecture XPU platforms. Through Intel technologies, our obstetric
screening assistant has achieved breakthroughs in terms of performance and
scalability and can now provide a more efficient means of smart obstetric
diagnosis to help medical institutions transition from conventional ultrasound
to smart ultrasound and assist doctors
in precise and efficient work to improve patient outcomes.”
Zhou Guoyi
Head of SonoScape Medical Innovation Research Center
About SonoScape
Founded in 2002 in Shenzhen, China, SonoScape has committed itself to “Caring
for Life through Innovation” by providing ultrasound and endoscopy solutions.
With seamless support, SonoScape provides worldwide sales and service in more
than 130 countries, benefiting local hospitals and doctors with comprehensive
imaging diagnostic evidence and technical support. Investing 20 percent of
total revenue into R&D annually, SonoScape has continuously introduced new
medical products and technologies into the market each year. It now expands
into seven R&D centers in Shenzhen, Shanghai, Harbin, Wuhan, Tokyo, Seattle,
and Silicon Valley. For more information, please visit our official website
www.sonoscape.com.
About Intel
Intel (Nasdaq: INTC) is an industry leader, creating worldchanging technology
that enables global progress and enriches lives. Inspired by Moore’s Law, we
continuously work to advance the design and manufacturing of semiconductors to
help address our customers’ greatest challenges. By embedding intelligence in
the cloud, network, edge, and every kind of computing device, we unleash the
potential of data to transform business and society for the better. To learn
more about Intel’s innovations, go to
newsroom.intel.com and
intel.com.
Solution provided by:
-
Efficiency increase claim of 50% is based on assessment data after clinical evaluation from 18 doctors of intermediate & senior experience in 5 medical facilities after 1 month period.
Reduction in workload claim of 70% based on evaluation of necessary steps to complete medical checkup using standard operation procedures vs. S-Fetus. -
For more information about S-Fetus 4.0 Obstetric Screening Assistant, please visit https://www.sonoscape.com/html/2020/exceed_0715/113.html
-
Test results provided by SonoScape. Test configuration: Intel® Core™ i7-1185GRE processor @ 2.80GHz, Intel Iris® Xe graphics @ 1.35 GHz, 96EU, Ubuntu 20.04, Intel® oneAPI DPC++/C++ Compiler, Intel® DPC++ Compatibility Tool, Intel® oneAPI DPC++ Library, Intel® Integrated Performance Primitives, Intel® VTune™ Profiler
-
Wells, P. N. T., “Physical Principles of Ultrasonic Diagnosis.” Medical and Biological Engineering 8, No. 2 (1970): 219–219.
-
https://www.fortunebusinessinsights.com/industry-reports/ultrasound-equipment-market-100515
-
Shengfeng Liu, et al., “Deep Learning in Medical Ultrasound Analysis: A Review.” Engineering 5, No. 2 (2019): 261–275
-
Test results provided by SonoScape. See backup for testing configurations.
-
Luo, Dandan, et al., “A Prenatal Ultrasound Scanning Approach: One-Touch Technique in Second and Third Trimesters.” Ultrasound Med Biol. 47, No. 8 (2021): 2258–2265.
https://www.researchgate.net/publication /351951854_A_Prenatal_Ultrasound_Scanning_Approach_One- Touch_Technique_in_Second_and_Third_Trimesters
Backup
Testing by SonoScape as of Sep 3, 2021. Test configuration: Intel® Core™
i7-1185GRE processor @ 2.80GHz, with or without Intel Iris® Xe graphics @ 1.35
GHz, 96EU, Ubuntu 20.04, Intel® oneAPI
DPC++/C++ Compiler, Intel® DPC++ Compatibility Tool, Intel® oneAPI DPC++
Library, Intel® Integrated Performance Primitives, Intel® VTune™ Profiler
Notices and Disclaimers
Performance varies by use, configuration, and other factors. Learn more at
www.Intel.com/PerformanceIndex
Performance results are based on testing as of dates shown in configurations
and may not reflect all publicly available updates. See backup for
configuration details. No product or component can be absolutely secure.
Your costs and results may vary.
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Intel does not control or audit third-party data. You should consult other
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trademarks of Intel Corporation or its subsidiaries. Other names and brands
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0422/EOH/MESH/PDF 350912-001US
References
- SonoScape
- OpenCV - Wikipedia
- Ultrasound Equipment Market Size, Share | Global Report [2028]
- Heterogeneous Programming Using oneAPI
- P60 - Trolley Color Doppler - SonoScape
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