Fetal brain MRI: segmentation and biometric analysis of the posterior fossa

This paper presents a novel approach to fetal magnetic resonance image segmentation and biometric analysis of the posterior fossa's midline structures. We developed a semi-automatic segmentation method (based on a region growing technique) and tested the algorithm on images of 104 normal fetuses. Using the segmented regions of interest (posterior fossa, vermis, and brainstem), we computed four relative area ratios. Statistical and clinical analysis of our results showed that the relative development of these structures appears to be independent of pregnancy term. In an additional study of 23 pathological cases, one of the four measurements was always significantly different from the corresponding value observed in normal cases.

[1]  J. Ballard,et al.  A simplified score for assessment of fetal maturation of newly born infants. , 1979, The Journal of pediatrics.

[2]  G. Hagemann,et al.  Fast, accurate, and reproducible automatic segmentation of the brain in T1‐weighted volume MRI data , 1999, Magnetic resonance in medicine.

[3]  E. Aylward,et al.  Area and volume measurement of posterior fossa structures in MRI. , 1991, Journal of psychiatric research.

[4]  J C Birnholz.  Newborn cerebellar size. , 1982, Pediatrics.

[5]  G. Sebag,et al.  Magnetic resonance imaging of the fetus , 1998, Pediatric Radiology.

[6]  J. Khoury,et al.  New Ballard Score, expanded to include extremely premature infants. , 1991, The Journal of pediatrics.

[7]  G. G. Cameron,et al.  Split-and-merge segmentation of magnetic resonance medical images: performance evaluation and extension to three dimensions. , 1998, Computers and biomedical research, an international journal.

[8]  B. Darlow,et al.  Cerebellar vermis diameter at cranial sonography for assessing gestational age in low-birth-weight infants , 1999, Pediatric Radiology.

[9]  T R Johnson,et al.  Gestational age assessment in preterm neonates weighing less than 1500 grams. , 1991, Pediatrics.

[10]  A. Ghidini,et al.  Dilated subarachnoid cisterna ambiens: a potential sonographic sign predicting cerebellar hypoplasia. , 1996, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[11]  A Dromigny-Badin,et al.  A pyramidal approach for automatic segmentation of multiple sclerosis lesions in brain MRI. , 1998, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[12]  G D Pearlson,et al.  Magnetic resonance imaging measurement of posterior fossa structures in schizophrenia. , 1994, The American journal of psychiatry.

[13]  Robert T. Schultz,et al.  Segmentation and Measurement of the Cortex from 3D MR Images , 1998, MICCAI.

[14]  R M Steiger,et al.  Biometry of the fetal cisterna magna: estimates of the ability to detect trisomy 18 , 1995, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[15]  P. Sonigo,et al.  MR imaging of fetal cerebral anomalies , 1998, Pediatric Radiology.

[16]  R. Quisling,et al.  The neonatal cisterna magna: ultrasonic evaluation. , 1983, Radiology.

[17]  P. Doubilet,et al.  Sonography of the fetal posterior fossa: false appearance of mega-cisterna magna and Dandy-Walker variant. , 1994, Radiology.

[18]  C. Garel,et al.  [Prenatal MRI of corpus callosum agenesis. Study of 20 cases with neuropathological correlations]. , 1998, Journal de radiologie.

[19]  Benoit M. Dawant,et al.  Morphometric analysis of white matter lesions in MR images: method and validation , 1994, IEEE Trans. Medical Imaging.

[20]  M. Stella Atkins,et al.  Fully automatic segmentation of the brain in MRI , 1998, IEEE Transactions on Medical Imaging.

[21]  Max A. Viergever,et al.  Multiscale Segmentation of Three-Dimensional MR Brain Images , 1999, International Journal of Computer Vision.

[22]  P Dastidar,et al.  Applicability of semi-automatic segmentation for volumetric analysis of brain lesions. , 1998, Journal of medical engineering & technology.

[23]  Benoit M. Dawant,et al.  Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations. I. Methodology and validation on normal subjects , 1999, IEEE Transactions on Medical Imaging.