Int J Comput Assist Radiol Surg - Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans.


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RPOSE: The subarachnoid space (SAS) lies between the arachnoid membrane and the pia mater of the human brain, normally filled with cerebrospinal fluid (CSF). Subarachnoid hemorrhage (SAH) is a serious complication of neurological disease that can have high mortality and high risk of disability. Computed tomography (CT) head scans are often used for diagnosing SAH which may be difficult when the hemorrhage is small or subtle. A computer-aided diagnosis system from CT images is thus developed to augment image interpretation.METHODS: Supervised learning using the probability of distance features of several landmarks was employed to recognize SAS. For each CT image, the SAS was approximated in four steps: (1) Landmarks including brain boundary, midsagittal plane (MSP), anterior and posterior intersection points of brain boundary with the MSP, and superior point of the brain were extracted. (2) Distances to all the landmarks were calculated for every pixel in the CT image, and combined to construct a high-dimensional feature vector. (3) Using head CT images with manually delineated SAS as training dataset, the prior probabilities of distances for pixels within SAS and non-SAS were computed. (4) Any pixel of a head CT scan in the testing dataset was classified as an SAS or non-SAS pixel in a Bayesian decision framework based on its distance features.RESULTS: The proposed method was validated on clinical head CT images by comparison with manual segmentation. The results showed that the automated method is consistent with the gold standard. Compared with elastic registration based on grayscale information, the proposed method was less affected by grayscale variation between normal controls and patients. Compared with manual delineation, the average spatial overlap, relative overlap, and similarity index were, respectively, 89, 63, and 76% for the automatic SAS approximation of the 69 head CT scans tested. The proposed method was tested for SAH detection and yielded a sensitivity of 100% and a specificity of 92%.CONCLUSION: Automated SAH detection with high sensitivity was shown feasible in a prototype computer-aided diagnosis system. The proposed method may be extended for computer-aided diagnosis of several CSF-related diseases relevant to SAS abnormalities.

Resumo Limpo

rpose subarachnoid space sas lie arachnoid membran pia mater human brain normal fill cerebrospin fluid csf subarachnoid hemorrhag sah serious complic neurolog diseas can high mortal high risk disabl comput tomographi ct head scan often use diagnos sah may difficult hemorrhag small subtl computeraid diagnosi system ct imag thus develop augment imag interpretationmethod supervis learn use probabl distanc featur sever landmark employ recogn sas ct imag sas approxim four step landmark includ brain boundari midsagitt plane msp anterior posterior intersect point brain boundari msp superior point brain extract distanc landmark calcul everi pixel ct imag combin construct highdimension featur vector use head ct imag manual delin sas train dataset prior probabl distanc pixel within sas nonsa comput pixel head ct scan test dataset classifi sas nonsa pixel bayesian decis framework base distanc featuresresult propos method valid clinic head ct imag comparison manual segment result show autom method consist gold standard compar elast registr base grayscal inform propos method less affect grayscal variat normal control patient compar manual delin averag spatial overlap relat overlap similar index respect automat sas approxim head ct scan test propos method test sah detect yield sensit specif conclus autom sah detect high sensit shown feasibl prototyp computeraid diagnosi system propos method may extend computeraid diagnosi sever csfrelat diseas relev sas abnorm

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