Automated segmentation of transcranial sonographic images in the diagnostics of Parkinson's disease

Ultrasonics
Andrius SakalauskasSaulius Rutkauskas

Abstract

Images captured during routine clinical transcranial sonography (TCS) examination are of a low resolution, so can be confusing for diagnostic evaluations. Manual segmentation of brain structures (areas of the midbrain and substantia nigra (SN)) that are of special interest cause inter-observer and intra-observer variability, thus restricting the reliability of Parkinson disease (PD) diagnostics. This paper presents a new technique for automated segmentation applicable to low resolution sonographic images, and particularly to brain structures related to PD. The segmentation was performed by a modified shape-based active contour (AC) segmentation algorithm. In order to suppress the speckle noise and to improve the AC segmentation, a pre-processing technique based on the averaging of adjusted spatially varying TCS images is proposed. The latter technique was tested on clinical TCS images. The results of the automated segmentation were compared with the manual markings. Two experts on the 40TCS images performed these markings. The comparison showed that an automated method is effective when segmentation of the midbrain is performed (averaged overlap between regions obtained automatically and outlined manually was 73.10±7.45%). The ...Continue Reading

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Citations

Nov 26, 2015·Ultrasound in Medicine & Biology·Andrius SakalauskasDaiva Rastenytė
May 27, 2015·Current Neurology and Neuroscience Reports·Andrea PilottoDaniela Berg
Jul 26, 2014·AJNR. American Journal of Neuroradiology·D SkoloudíkR Herzig
Jan 14, 2018·Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine·Andrius SakalauskasArūnas Lukoševičius
Oct 4, 2017·The World Journal of Biological Psychiatry : the Official Journal of the World Federation of Societies of Biological Psychiatry·Carsten DrepperManfred Gerlach
Mar 2, 2021·PloS One·Karl AounGabrielle Todd

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