Computer-aided detection/diagnosis (CAD) is a key component of routine clinical practice, increasingly used for detection, interpretation, quantification and decision support. Despite a critical need, there is no clinically accepted CAD system for stroke yet. Here we introduce a CAD system for hemorrhagic stroke. This CAD system segments, quantifies, and displays hematoma in 2D/3D, and supports evacuation of hemorrhage by thrombolytic treatment monitoring progression and quantifying clot removal. It supports seven-step workflow: select patient, add a new study, process patient's scans, show segmentation results, plot hematoma volumes, show 3D synchronized time series hematomas, and generate report. The system architecture contains four components: library, tools, application with user interface, and hematoma segmentation algorithm. The tools include a contour editor, 3D surface modeler, 3D volume measure, histogramming, hematoma volume plot, and 3D synchronized time-series hematoma display. The CAD system has been designed and implemented in C++. It has also been employed in the CLEAR and MISTIE phase-III, multicenter clinical trials. This stroke CAD system is potentially useful in research and clinical applications, particularly for clinical trials.
MendelowADGregsonBARowanEN. STICH II Investigators. Early surgery versus initial conservative treatment in patients with spontaneous supratentorial lobar intracerebral haematomas (STICH II): a randomised trial. Lancet. 2013; 382 (9890): 397–408. doi: 10.1016/S0140-6736(13)60986-1.
2.
ZiaiWCTuhrimSLaneK. A multicenter, randomized, double-blinded, placebo-controlled phase III study of Clot Lysis Evaluation of Accelerated Resolution of Intraventricular Hemorrhage (CLEAR III). Int J Stroke. 2013. doi: 10.1111/ijs.12097.
3.
DoiK. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 2007; 31 (4–5): 198–211. doi: 10.1016/j.compmedimag.2007.02.002.
4.
GigerMLChanHPBooneJ. Anniversary paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM. Med Phys. 2008; 35 (12): 5799–5820. doi: 10.1118/1.3013555.
5.
ShiraishiJLiQAppelbaumD. Development of a computer-aided diagnostic scheme for detection of interval changes in successive whole-body scans. Med Phys. 2006; 34: 25–36. doi: 10.1118/1.2401044.
6.
ElmoreJG. Influence of computer-aided detection on performance of screening mammography. N Engl J Med.2007; 356 (14): 1399–1409. doi: 10.1056/NEJMoa066099.
7.
JiangY. Computer-aided diagnosis of breast cancer in mammography: evidence and potential. Technol Cancer Res Treat. 2002; 1 (3): 211–216.
8.
NishikawaRM. Current status and future directions of computer-aided diagnosis in mammography. Comput Med Imaging Graph. 2007; 31 (4–5): 224–235. doi: 10.1016/j.compmedimag.2007.02.009.
9.
ZhengB. Computer-aided diagnosis in mammography using content based image retrieval approaches: Current status and future perspectives. Algorithms. 2009: 2 (2): 828–849. doi: 10.3390/a2020828.
10.
ManiANapelSPaikDS. Computed Tomography Colonography: Feasibility of Computer-aided polyp detection in a “First Reader” paradigm. Abdominal Imaging. J Comput Assist Tomogr. 2004; 28 (3): 318–326. doi: 10.1097/00004728-200405000-00003.
11.
SummersRMYaoJHJohnsonCD. CT colonography with computer-aided detection: Automated recognition of ileocecal valve to reduce number of false-positive detections. Radiology. 2004; 233 (1); 266–272. doi: 10.1148/radiol.2331031326.
12.
MoriKNikiNKondoT. Development of a novel computer-aided diagnosis system for automatic discrimination of malignant from benign solitary pulmonary nodules on thin-section dynamic computed tomography. J Comput Assist Tomogr. 2005; 29 (2): 215–222. doi: 10.1097/01.ret.0000155668.28514.01.
13.
Van GinnekenBTer Haar RomenyBMViegeverMA. Computer-aided diagnosis in chest radiography: A survey. IEEE Trans Med Imag. 2001; 20 (12): 1228–1241. doi: 10.1109/42.974918.
14.
NowinskiWLQianGBhanu PrakashKN. A CAD system for acute ischemic stroke image processing. Int J Comput Assist Radiol Surg. 2007; 2 (Suppl. 1): 220–222.
15.
NowinskiWLQianGBhanu PrakashKN: Analysis of ischemic stroke MR images by means of brain atlases of anatomy and blood supply territories. Acad Radiol. 2006; 13 (8): 1025–1034. doi: 10.1016/j.acra.2006.05.009.
16.
NowinskiWLQianGYBhanu PrakashKN. Design and development of a computer aided diagnosis system for processing of acute ischemic stroke MR images. WSEAS Trans Biol Biomed. 2006; 3 (6): 401–407.
17.
NowinskiWLGuptaVQianGY. Automatic detection, localization and volume estimation of ischemic infarcts in non-contrast CT scans: method and preliminary results. Invest Radiol. 2013; 48 (9): 661–670. doi: 10.1097/RLI.0b013e31828d8403.
NowinskiWLQianGBhanu PrakashKN. Stroke Suite: CAD systems for acute ischemic stroke, hemorrhagic stroke, and stroke in ER. In: GaoX. Medical Imaging and Informatics (eds GaoXMullerHLoomersMComleyRLuoS) Lecture Notes in Computer Science. 2008. doi: 10.4987:377–386.1097/ RLI.0b013e31828d8403.
20.
LorensenWEClineHE. Marching Cubes: A high resolution 3D surface construction algorithm. Comput Graph. 1987; 21 (4): 163–169. doi: 10.1145/37402. 37422.
21.
Bhanu PrakashKNMorganTCHanleyDF. A brain parenchyma model based segmentation of intraventricular and intracerebral haemorrhage in CT scans. Neuroradiol J.2012; 25: 273–282.
22.
Bhanu PrakashKNHuJMorganT. Comparison of three segmentation techniques for intra-ventricular and intra-cerebral haemorrhages in unenhanced CT scans. J Comput Assist Tomogr. 2012; 36 (1): 109–20. doi: 10.1097/RCT.0b013e318245c1fa.
23.
Bhanu PrakashKNZhouSMorganTCHanleyDF. Segmentation and quantification of intra- ventricular/cerebral hemorrhage in CT scans by modified distance regularized level set evolution technique. Int J Comput Assist Radiol Surg. 2012; 7 (5): 785–798. doi: 10.1007/s11548-012-0670-0.
24.
NowinskiWLGomolkaRSQianG. Characterization of intraventricular and intracerebral hematomas in non-contrast CT. Neuroradiol J.2014; 27 (3): 299–315. doi: 10.15274/NRJ-2014-10042.
FarjamRParmarHANollDC. An approach for computer-aided detection of brain metastases in post-Gd T1-W MRI. Magn Reson Imaging. 2012; 30 (6): 824–36. doi: 10.1016/j.mri.2012.02.024.
27.
IlkkoESuomiKKarttunenA. Computer-assisted diagnosis by temporal subtraction in postoperative brain tumor patients - A feasibility study. Acad Radiol. 2004: 11 (8): 887–893. doi: 10.1016/S1076-6332(04)00232-6.
28.
JensenTRSchmaindaKM. Computer-aided detection of brain tumor invasion using multiparametric MRI. J Magn Reson Imaging. 2009; 30 (3): 481–489. doi: 10.1002/jmri.21878.
29.
YangSNamYKimMO. Computer-aided detection of metastatic brain tumors using magnetic resonance black-blood imaging. Invest Radiol. 2013; 48 (2): 113–119. doi: 10.1097/RLI.0b013e318277f078.
30.
ArimuraHLiQKorogiY. Computerized detection of intracranial aneurysms for three-dimensional MR angiography: Feature extraction of small protrusions based on a shape-based difference image technique. Med Phys. 2006; 33: 394–401. doi: 10.1118/1.2163 389.
31.
BianWHessCPChangSM. Computer-aided detection of radiation-induced cerebral microbleeds on susceptibility-weighted MR images. Neuroimage Clin. 2013; 2: 282–290. doi: 10.1016/j.nicl.2013.01.012.
32.
YamamotoDArimuraHKakedaS. Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine. Comput Med Imaging Graph. 2010; 34 (5): 404–413. doi: 10.1016/j.compmedimag.2010.02.001.
33.
PlateAAhmadiSAPaulyO. Three-dimensional sonographic examination of the midbrain for computer-aided diagnosis of movement disorders. Ultrasound Med Biol. 2012; 38 (12): 2041–2050. doi: 10.1016/j.ultrasmedbio.2012.07.017.
34.
ChavesRRamirezJGórrizJM. SVM-based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting. Neurosci Lett. 2009; 461 (3): 293–297. doi: 10.1016/j.neulet.2009.06.052.
KennedyDNHaselgroveCMakrisN. WebParc: a tool for analysis of the topography and volume of stroke from MRI. Med Biol Eng Comput. 2010; 48 (3): 215–28. doi: 10.1007/s11517-009-0571-8.
37.
UchiyamaYAsanoTKatoH. Computer-aided diagnosis for detection of lacunar infarcts on MR images: ROC analysis of radiologists' performance. J Digit Imaging. 2012; 25 (4): 497–503. doi: 10.1007/s10278-011-9444-4.
38.
AfonsoDSeabraJSuriJS. A CAD system for atherosclerotic plaque assessment. Conf Proc IEEE Eng Med Biol Soc.2012; 2012: 1008–1011. doi: 10.1109/EMBC.2012.6346104.
39.
ChanTHuangHK. Effect of a computer-aided diagnosis system on clinicians' performance in detection of small acute intracranial hemorrhage on computed tomography. Acad Radiol. 2008; 15 (3): 290–299. doi: 10.1016/j.acra.2007.09.022.
40.
TangFHNgDKChowDH. An image feature approach for computer-aided detection of ischemic stroke. Comput Biol Med.2011; 41 (7): 529–536. doi: 10.1016/j.compbiomed.2011.05.001.
