Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine

PloS One
Jorge Arturo Zavala BojorquezAlain Lalande

Abstract

Tissue segmentation and classification in MRI is a challenging task due to a lack of signal intensity standardization. MRI signal is dependent on the acquisition protocol, the coil profile, the scanner type, etc. While we can compute quantitative physical tissue properties independent of the hardware and the sequence parameters, it is still difficult to leverage these physical properties to segment and classify pelvic tissues. The proposed method integrates quantitative MRI values (T1 and T2 relaxation times and pure synthetic weighted images) and machine learning (Support Vector Machine (SVM)) to segment and classify tissues in the pelvic region, i.e.: fat, muscle, prostate, bone marrow, bladder, and air. Twenty-two men with a mean age of 30±14 years were included in this prospective study. The images were acquired with a 3 Tesla MRI scanner. An inversion recovery-prepared turbo spin echo sequence was used to obtain T1-weighted images at different inversion times with a TR of 14000 ms. A 32-echo spin echo sequence was used to obtain the T2-weighted images at different echo times with a TR of 5000 ms. T1 and T2 relaxation times, synthetic T1- and T2-weighted images and anatomical probabilistic maps were calculated and used as i...Continue Reading

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Jun 21, 2019·Magnetic Resonance in Medical Sciences : MRMS : an Official Journal of Japan Society of Magnetic Resonance in Medicine·Yutaka KatoShinji Naganawa
Apr 22, 2020·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Soraya GavazziAstrid L H M W van Lier
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Jul 14, 2021·Radiological Physics and Technology·Takafumi NemotoNaoyuki Shigematsu

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Methods Mentioned

BETA
imaging technique

Software Mentioned

libLinear

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