Segmentation of prostate and prostate zones using deep learning : A multi-MRI vendor analysis.

PURPOSE: Develop a deep-learning-based segmentation algorithm for prostate and its peripheral zone (PZ) that is reliable across multiple MRI vendors. METHODS: This is a retrospective study. The dataset consisted of 550 MRIs (Siemens-330, General Electric[GE]-220). A multistream 3D convolutional neural network is used for automatic segmentation of the prostate and its PZ using T2-weighted (T2-w) MRI. Prostate and PZ were manually contoured on axial T2­w. The network uses axial, coronal, and sagittal T2­w series as input. The preprocessing of the input data includes bias correction, resampling, and image normalization. A dataset from two MRI vendors (Siemens and GE) is used to test the proposed network. Six different models were trained, three for the prostate and three for the PZ. Of the three, two were trained on data from each vendor separately, and a third (Combined) on the aggregate of the datasets. The Dice coefficient (DSC) is used to compare the manual and predicted segmentation. RESULTS: For prostate segmentation, the Combined model obtained DSCs of 0.893 ± 0.036 and 0.825 ± 0.112 (mean ± standard deviation) on Siemens and GE, respectively. For PZ, the best DSCs were from the Combined model: 0.811 ± 0.079 and 0.788 ± 0.093. While the Siemens model underperformed on the GE dataset and vice versa, the Combined model achieved robust performance on both datasets. CONCLUSION: The proposed network has a performance comparable to the interexpert variability for segmenting the prostate and its PZ. Combining images from different MRI vendors on the training of the network is of paramount importance for building a universal model for prostate and PZ segmentation.

Left-Lateralized Contributions of Saccades to Cortical Activity During a One-Back Word Recognition Task.

Saccadic eye movements are an inherent component of natural reading, yet their contribution to information processing at subsequent fixation remains elusive. Here we use anatomically-constrained magnetoencephalography (MEG) to examine cortical activity following saccades as healthy human subjects engaged in a one-back word recognition task. This activity was compared with activity following external visual stimulation that mimicked saccades. A combination of procedures was employed to eliminate saccadic ocular artifacts from the MEG signal. Both saccades and saccade-like external visual stimulation produced early-latency responses beginning ~70 ms after onset in occipital cortex and spreading through the ventral and dorsal visual streams to temporal, parietal and frontal cortices. Robust differential activity following the onset of saccades vs. similar external visual stimulation emerged during 150-350 ms in a left-lateralized cortical network. This network included: (i) left lateral occipitotemporal (LOT) and nearby inferotemporal (IT) cortex; (ii) left posterior Sylvian fissure (PSF) and nearby multimodal cortex; and (iii) medial parietooccipital (PO), posterior cingulate and retrosplenial cortices. Moreover, this left-lateralized network colocalized with word repetition priming effects. Together, results suggest that central saccadic mechanisms influence a left-lateralized language network in occipitotemporal and temporal cortex above and beyond saccadic influences at preceding stages of information processing during visual word recognition.

Relationship Between Optic Nerve Protrusion Measured by OCT and MRI and Papilledema Severity.

PURPOSE: To develop measures of optic nerve protrusion length (NPL) from optical coherence tomography (OCT) and magnetic resonance imagining (MRI) and compare these measures with papilledema severity in idiopathic intracranial hypertension (IIH). METHODS: Optical coherence tomography and MRI scans were obtained from 11 newly diagnosed untreated IIH patients (30 ± 10 years; body mass index [BMI] 36 ± 4 kg/m2). Optic nerve protrusion length was measured for each eye using OCT and MRI independently. The relationship between the NPL measures and their association with the Frisen scale for papilledema severity were assessed. Two different OCT-based measures of NPL were derived to assess the influence of the retinal thickness on the association with papilledema severity. Additional OCT scans from 11 healthy subjects (38 ± 7 years) were analyzed to establish reliability of the NPL measurement. RESULTS: Optical coherence tomography and MRI measurements of NPL were significantly linearly correlated (R = 0.79, P < 0.0001). Measurements of NPL from OCT and MRI were significantly associated with Frisen papilledema grade (P < 0.0001). Mean OCT measurement of NPL in the papilledema cohort was significantly larger than in the healthy cohort (0.62 ± 0.24 vs. 0.09 ± 0.03 mm, P < 0.0001). CONCLUSIONS: Significant linear correlation between OCT and MRI measurements of NPL supports the reliability of the OCT-based measurements of NPL in papilledema. Significant association between the papilledema grade and OCT- and MRI-based measurements of NPL highlights the potential of NPL as an objective and more sensitive marker of papilledema severity than the Frisen scale.