Web-based Computer-aided-diagnosis (CAD) System for. Bone Age Assessment (BAA) of Children. Aifeng Zhang1, Joshua Uyeda1, Sinchai Tsao1, Kevin Ma1, ...
Web-based Computer-aided-diagnosis (CAD) System for Bone Age Assessment (BAA) of Children Aifeng Zhang1, Joshua Uyeda1, Sinchai Tsao1, Kevin Ma1, Linda A. Vachon2, Brent J. Liu1, H.K. Huang1 1
Image Processing & Informatics Lab, Department of Radiology, ISI/USC, Marina Del Rey, CA 90292 2 Los Angeles County Women’s and Children’s Hospital Los Angeles, CA 90033
ABSTRACT Bone age assessment (BAA) of children is a clinical procedure frequently performed in pediatric radiology to evaluate the stage of skeletal maturation based on a left hand and wrist radiograph. The most commonly used standard: Greulich and Pyle (G&P) Hand Atlas was developed 50 years ago and exclusively based on Caucasian population. Moreover, inter- & intra-observer discrepancies using this method create a need of an objective and automatic BAA method. A digital hand atlas (DHA) has been collected with 1,400 hand images of normal children from Asian, African American, Caucasian and Hispanic descends. Based on DHA, a fully automatic, objective computer-aided-diagnosis (CAD) method was developed and it was adapted to specific population. To bring DHA and CAD method to the clinical environment as a useful tool in assisting radiologist to achieve higher accuracy in BAA, a web-based system with direct connection to a clinical site is designed as a novel clinical implementation approach for online and real time BAA. The core of the system, a CAD server receives the image from clinical site, processes it by the CAD method and finally, generates report. A web service publishes the results and radiologists at the clinical site can review it online within minutes. This prototype can be easily extended to multiple clinical sites and will provide the foundation for broader use of the CAD system for BAA.
1. INTRODUCTION Bone age assessment (BAA) of children is a clinical procedure frequently performed in pediatric radiology to evaluate the stage of skeletal maturation based on a left hand and wrist radiograph. The most commonly used standard: Greulich and Pyle (G&P) Hand Atlas was developed 50 years ago and exclusively based on Caucasian population. [1] Moreover, inter- & intra-observer discrepancies using this method create a need of an objective and automatic BAA method. A digital hand atlas (DHA) has been collected with 1,400 hand images of normal children from Asian, African American, Caucasian and Hispanic descends. Based on DHA, a fully automatic, objective computer-aided-diagnosis (CAD) method was developed based on phalangeal and carpal bone features. Adapted to specific populations, the CAD method achieved high BAA accuracy. [2-7]
Medical Imaging 2008: PACS and Imaging Informatics, edited by Katherine P. Andriole, Khan M. Siddiqui, Proc. of SPIE Vol. 6919, 69190H, (2008) · 1605-7422/08/$18 · doi: 10.1117/12.770785
Proc. of SPIE Vol. 6919 69190H-1 2008 SPIE Digital Library -- Subscriber Archive Copy
To bring DHA and CAD method to the clinical environment as a useful tool in assisting radiologist to achieve higher accuracy in BAA, a client-server system with direct connection to a clinical site is designed as a novel clinical implementation approach for online and real time BAA.
2. METHODS 2.1 Standard Clinical Workflow The standard clinical workflow of BAA at clinical site is as following: Hand X-ray from the CR modality is sent to gateway and then to PACS server. Radiologist reviews images on PACS WS and assesses the bone age (Figure 1). [8]
CR Modality
Gateway
PACS Server
PACS Workstation
Figure 1. Normal Clinical Workflow 2.2 Web-based BAA CAD Clinical Implementation To minimize the disruption of the PACS clinical workflow, a client-server system was developed as a novel clinical implementation approach. Utilizing web technology and DICOM (Digital Imaging and Communications in Medicine) standard, CAD method was integrated with PACS for online BAA. [9,10] In the CAD system clinical implementation workflow (Figure 2), a second copy of the hand image from CR modality is sent to the CAD server with secure internet connection. The CAD server receives the image and processes it. The CAD report is then generated automatically. The radiologist at the clinical site can log into the website and review CAD results from a PACS WS. The web-based CAD report display application allows multiple users with specific patient list. For each radiologist, clinical decisions (without and with the aid of CAD) including readings and growth abnormality are captured and stored in the clinical database.
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LA County Women’s & Children’s Hospital Environment
CR Modality
PACS WS
PACS Server
CAD report on web
Gateway
BAA CAD Server
Radiologist decisions
Figure 2. Clinical Implementation Workflow
2.3 BAA CAD Server Design The core of the system, CAD server includes the following major components (Figure 3): DICOM receiver, CAD engine, web service and clinical database. The DICOM receiver utilizes the DCMTK toolkit to listen and receive the hand image from CR modality over the Internet using DICOM Storage protocol [11]. The CAD engine is then triggered to segment the hand images, extract bony features for automatic bone age assessment by fuzzy logic. The CAD report is then generated automatically at CAD server. The web service automatically updates the clinical database and publishes the CAD report for radiologist at the clinical site to review online.
BAA CAD Server [ ciinicai
I Database
DICOM image CAD CAD Receiver
Web
Engine Report Service
Figure 3. Major components in CAD server The purpose of the web GUI is for the radiologists to be able to select and view the DICOM hand images from the patient list. The GUI provides the race and gender of the patient and it allows the radiologists to enter their initial reading into the database after reviewing the original patient hand image. After the initial
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reading has been entered, the CAD BAA result is shown with: 1) CAD assessed bone age; 2) patient displayed in the normal development graph for the corresponding racial group and 3) the best match image found from the DHA. The radiologist is then allowed to input a second reading for the hand image after reviewing the CAD results. 2.3 Laboratory Simulation at IPILAB An existing PACS simulator developed at the laboratory simulates the PACS clinical workflow. This testbed system has been utilized to test the CAD server and PACS integration. Figure 4 shows the CAD server at left, and the four major components of the PACS simulator on the right: modality simulator, gateway, PACS server and PACS workstation (WS).
BAA CAD Server
Modality Simulator
Gateway
PACS Server
PACS WS
Figure 4. BAA CAD server and PACS simulator at IPILAB
3. RESULTS The CAD server and PACS integration has been successfully implemented on an existing PACS simulator developed at our lab. A number of clinical images were tested for the entire workflow. A fully automatic workflow was achieved without manual interaction. This system is currently being implemented in multiple clinical sites. The hand image is sent from the modality to the CAD system and the CAD results are displayed on the PACS WS for radiologists to review. Figure 5 shows an example of web GUI.
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Figure 5. Web GUI (a) Patient list, patient information table and radiologist’s initial reading; (b) First page with patient’s original hand image; (c) Second page with CAD results; Radiologist can input the second reading.
4. DISCUSSIONS AND CONCLUSIONS This paper presents a novel clinical implementation approach for a CAD system using web technology for online and real time BAA. It brings DHA and CAD methods to the clinical environment as a useful tool in assisting radiologist to achieve higher accuracy in BAA. This also allows data collection in the atlas to be continuously enriched with normal cases selected from the clinical environment. This web-based prototype can be easily extended to multiple clinical sites and will provide the foundation for broader use of the CAD system for BAA. By equivalence test on the means of two assessments (without and with aid of CAD), the statistical analysis will allow us to validate that the DHA and CAD can assist radiologists and residents to achieve higher accuracy of BAA.
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ACKNOWLEDGMENT This work has been supported by NIH R01 EB 00298.
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