Automated thalamic nuclei segmentation using multi-planar cascaded convolutional neural networks

Abstract

Purpose-To develop a fast and accurate convolutional neural network based method for segmentation of thalamic nuclei.Methods-A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on conventional and white-matter-nulled (WMn) magnetization prepared rapid gradient echo (MPRAGE) data. A single network was optimized to work with images from healthy controls and patients with multiple sclerosis (MS) and essential tremor (ET), acquired at both 3T and 7T field strengths. WMn-MPRAGE images were manually delineated by a trained neuroradiologist using the Morel histological atlas as a guide to generate reference ground truth labels. Dice similarity coefficient and volume similarity index (VSI) were used to evaluate performance. Clinical utility was demonstrated by applying this method to study the effect of MS on thalamic nuclei atrophy.Results-Segmentation of each thalamus into twelve nuclei was achieved in under a minute. For 7T WMn-MPRAGE, the proposed method outperforms current state-of-the-art on patients with ET with statistically significant improvements in Dice for five nuclei (increase in the range of 0.05-0.18) and VSI for four nuclei (increase in the range of 0.05-0.19), while performing comparably

Mutually communicated model based on multi‐parametric MRI for automated segmentation and classification of prostate cancer

“…CAD systems for PCa comprise two main tasks: prostate segmentation and classification. [7][8][9][10][11][12][13] The segmentation task involves detecting the prostate's location and boundaries, whereas the classification's goal is to determine PCa type (e.g., benign, malignant). However, both tasks are still challenging.…”

“…Computer‐aided diagnosis (CAD) has been a rapidly growing and active research area over the past two decades. Several CAD systems for the radiological assessment of thalamic nuclei, left‐ventricular, and prostate malignancies have been developed, 10–12 and encouraging results have been reported. CAD systems for PCa comprise two main tasks: prostate segmentation and classification 7–13 .…”

“…Several CAD systems for the radiological assessment of thalamic nuclei, left‐ventricular, and prostate malignancies have been developed, 10–12 and encouraging results have been reported. CAD systems for PCa comprise two main tasks: prostate segmentation and classification 7–13 . The segmentation task involves detecting the prostate's location and boundaries, whereas the classification's goal is to determine PCa type (e.g., benign, malignant).…”