Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images.

BackgroundLiver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion. Image registration can improve image interpretation and lesion co-localization. We assessed the performance of a convolutional neural network algorithm to register cross-sectional liver imaging series and compared its performance to manual image registration.MethodsThree hundred fourteen patients, including internal and external datasets, who underwent gadoxetate disodium-enhanced magnetic resonance imaging for clinical care from 2011 to 2018, were retrospectively selected. Automated registration was applied to all 2,663 within-patient series pairs derived from these datasets. Additionally, 100 within-patient series pairs from the internal dataset were independently manually registered by expert readers. Liver overlap, image correlation, and intra-observation distances for manual versus automated registrations were compared using paired t tests. Influence of patient demographics, imaging characteristics, and liver uptake function was evaluated using univariate and multivariate mixed models.ResultsCompared to the manual, automated registration produced significantly lower intra-observation distance (p < 0.001) and higher liver overlap and image correlation (p < 0.001). Intra-exam automated registration achieved 0.88 mean liver overlap and 0.44 mean image correlation for the internal dataset and 0.91 and 0.41, respectively, for the external dataset. For inter-exam registration, mean overlap was 0.81 and image correlation 0.41. Older age, female sex, greater inter-series time interval, differing uptake, and greater voxel size differences independently reduced automated registration performance (p ≤ 0.020).ConclusionA fully automated algorithm accurately registered the liver within and between examinations, yielding better liver and focal observation co-localization compared to manual registration

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization

Small bowel motility quantitation using MRI and its relationship to gastrointestinal symptoms

The small bowel is difficult to analyse due to its deep anatomical location and the large variation seen in individuals, in regard to both anatomy and function including motility. Dynamic MRI allows small bowel motility to be captured and visually assessed by radiologists, but there is often large inter-observer variation and a lack of complicated motility patterns being investigated. This thesis aims to explore the link between abnormal motility and gastrointestinal (GI) symptoms in Crohn’s disease (CD) and irritable bowel syndrome (IBS) using MRI. Firstly, a scan duration of 15 seconds and a temporal resolution of 1 image per second were shown to be sufficient for robust small bowel MRI motility measurements. Next, a validation study confirmed an association between aberrant motility and CD patient symptoms, particularly diarrhoeal stools (rho = -0.29). The strongest association was in patients with higher symptom severity (rho = -0.633). Building on this work, more complex motility metrics were developed and compared to subjective radiological scoring. Spatial and temporal variation were found to be associated with CD patient symptoms and were also particularly difficult to visually assess. The motility metrics were applied in clinical IBS data to explore differences in IBS subgroups. Significantly reduced temporal variation of motility (P < 0.001) and area of motile bowel (P < 0.001) was found in IBS-C (constipation-predominant) compared to IBS-M (mixed constipation and diarrhoea). Finally, texture analysis (TA) terminal ileum (TI) to colon ratios were found to be higher for TA contrast (P = 0.005) and lower for TA energy (P = 0.03) in IBS-C compared to healthy controls (HCs). Ascending colon diameter was shown to be significantly larger in IBS-C than HCs (P = 0.005)

Automated registration of breast lesions in temporal pairs of mammograms for interval change analysisâ local affine transformation for improved localization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134991/1/mp6134.pd

A review of artificial intelligence in prostate cancer detection on imaging

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care

Breast Mass Characterization Using 3‐Dimensional Automated Ultrasound as an Adjunct to Digital Breast Tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135628/1/jum201332193.pd

Signs of progression:MR image analysis for the management of low-grade glioma

Every year approximately one thousand people in the Netherlands are diagnosedwith diffuse glioma, a type of infiltrative brain tumor that originates from theglial cells. There is no curative treatment available for adults diagnosed witha diffuse glioma, although surgical resection, radiotherapy and chemotherapyare used to improve prognosis and decrease symptoms. Low-grade glioma canremain stable for long periods of time before, inevitably, malignant progressionoccurs. The radiological assessment of glioma through magnetic resonanceimaging (MRI) plays an important role in the management of glioma. Inthis thesis I explore the role of quantitative measurements, emerging imagingmarkers and predictive modelling in the management of glioma. These methodscan aid the radiologist to predict the timing, location and severity of tumorprogression, to ultimately improve the quality of life for glioma patients.<br/