Epilepsy, a common neurological disorder causing recurring seizures. Magnetic Resonance-guided Laser Interstitial Thermal Therapy (MRgLITT) is a promising minimally-invasive technique to ablate the target especially for drug resistance epilepsy. MRgLITT employs a laser fiber to ablate brain tissue through heat deposition, offering real-time monitoring through Magnetic Resonance (MR) thermometry images and precise treatment planning using MRI planning images. In this study, we developed an AI-based approach utilizing a U-Net model, a convolutional neural network architecture widely used for image to image translation, to predict MR thermometry images from anatomical MRI planning images from a dataset of 81 patients with mesial temporal lobe epilepsy. The model’s performance was evaluated on a test dataset using the structural similarity index (SSIM) and root mean squared error (RMSE).
To demonstrate the added predictive value of radiomic features to prostate radiology scoring scheme (PIRADS), a systematic approach is required to determine whether there is indeed latent predictive information of prostate cancer in diffusion-weighted magnetic resonance images (DW-MRI) that cannot be captured by radiologists’ visual interpretations alone. In this work, we propose a PI-RADS guided discovery radiomics solution where a predictive model for prostate cancer is built by discovering radiomic features that capture information on the phenotype of lesions, which is not visible to radiologists when using PI-RADS scoring system. We investigated patients with PI-RADS scores indicating presence or absence of significant prostate cancer separately and ran experiments on patients with DW-MRI followed by targeted biopsy, using first and second order quantitative imaging features. Our experiments on DW-MRI and pathology data of 50 patients show that the proposed approach improves the overall accuracy of prostate cancer diagnosis significantly compared to PI-RADS scores alone.
Prostate cancer is a leading cause of cancer-related death among men. Multiparametric magnetic resonance imaging has become an essential part of the diagnostic evaluation of prostate cancer. The internationally accepted interpretation scheme (Pi-Rads v2) has different algorithms for scoring of the transition zone (TZ) and peripheral zone (PZ) of the prostate as tumors can appear different in these zones. Computer-aided detection tools have shown different performances in TZ and PZ and separating these zones for training and detection is essential. The TZ-PZ segmentation which requires the segmentation of prostate whole gland and TZ is typically done manually. We present a fully automatic algorithm for delineation of the prostate gland and TZ in diffusion-weighted imaging (DWI) via a stack of fully convolutional neural networks. The proposed algorithm first detects the slices that contain a portion of prostate gland within the three-dimensional DWI volume and then it segments the prostate gland and TZ automatically. The segmentation stage of the algorithm was applied to DWI images of 104 patients and median Dice similarity coefficients of 0.93 and 0.88 were achieved for the prostate gland and TZ, respectively. The detection of image slices with and without prostate gland had an average accuracy of 0.97.
While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features that may not fully encapsulate the differences between cancerous and healthy tissue. Recently, the concept of discovery radiomics was introduced, where custom abstract features are discovered from readily available imaging data. We propose an evolutionary deep radiomic sequencer discovery approach based on evolutionary deep intelligence. Motivated by patient privacy concerns and the idea of operational artificial intelligence, the evolutionary deep radiomic sequencer discovery approach organically evolves increasingly more efficient deep radiomic sequencers that produce significantly more compact yet similarly descriptive radiomic sequences over multiple generations. As a result, this framework improves operational efficiency and enables diagnosis to be run locally at the radiologist’s computer while maintaining detection accuracy. We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically proven diagnostic data from the LIDC-IDRI dataset. The EDRS shows improved sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%) relative to previous radiomics approaches.
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