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 input features of a SVM for segmenting and classifying tissues within the pelvic region. The mean SVM classification accuracy across subjects was calculated for the different tissues: prostate (94.2%), fat (96.9%), muscle (95.8%), bone marrow (91%) and bladder (82.1%) indicating an excellent classification performance. However, the segmentation and classification for air (within the rectum) may not always be successful (mean SVM accuracy 47.5%) due to the lack of air data in the training and testing sets. Our findings suggest that SVM can reliably segment and classify tissues in the pelvic region.
Highlights
Tissue segmentation and classification is an important topic in Magnetic Resonance Imaging (MRI)
The sequences are used to obtain T1-weighted and T2-weighted images in order to compute the T1 and T2 relaxation maps. These maps serve as the input feature vectors of the Support Vector Machine (SVM) used for tissue segmentation-classification
The bladder classification accuracy was excellent (> 80%) for those SVM models including most of the features
Summary
Tissue segmentation and classification is an important topic in Magnetic Resonance Imaging (MRI). It helps to study anatomical structures, to develop surgical planning, plan radiation therapy and perform quantitative analyses [1,2]. Tissue classification on pelvic MRI design, data collection and analysis, decision to publish, or preparation of the manuscript
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