Gabriel Chartrand

The Importance of Skip Connections in Biomedical Image Segmentation

In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long skip connections are used to skip features from the contracting path to the expanding path in order to recover spatial information lost during downsampling. We extend FCNs by adding short skip connections, that are similar to the ones introduced in residual networks, in order to build very deep FCNs (of hundreds of layers). A review of the gradient flow confirms that for a very deep FCN it is beneficial to have both long and short skip connections. Finally, we show that a very deep FCN can achieve near-to-state-of-the-art results on the EM dataset without any further post-processing.

Effects of Insulin Glargine and Liraglutide Therapy on Liver Fat As Measured by Magnetic Resonance in Patients With Type 2 Diabetes: A Randomized Trial

OBJECTIVE This study determined the effects of insulin versus liraglutide therapy on liver fat in patients with type 2 diabetes inadequately controlled with oral agents therapy, including metformin. RESEARCH DESIGN AND METHODS Thirty-five patients with type 2 diabetes inadequately controlled on metformin monotherapy or in combination with other oral antidiabetic medications were randomized to receive insulin glargine or liraglutide therapy for 12 weeks. The liver proton density fat fraction (PDFF) was measured by MRS. The mean liver PDFF, the total liver volume, and the total liver fat index were measured by MRI. The Student t test, the Fisher exact test, and repeated-measures ANOVA were used for statistical analysis. RESULTS Insulin treatment was associated with a significant improvement in glycated hemoglobin (7.9% to 7.2% [62.5 to 55.2 mmol/mol], P = 0.005), a trend toward a decrease in MRS-PDFF (12.6% to 9.9%, P = 0.06), and a significant decrease in liver mean MRI-PDFF (13.8% to 10.6%, P = 0.005), liver volume (2,010.6 to 1,858.7 mL, P = 0.01), and the total liver fat index (304.4 vs. 209.3 % ⋅ mL, P = 0.01). Liraglutide treatment was also associated with a significant improvement in glycated hemoglobin (7.6% to 6.7% [59.8 to 50.2 mmol/mol], P < 0.001) but did not change MRS-PDFF (P = 0.80), liver mean MRI-PDFF (P = 0.15), liver volume (P = 0.30), or the total liver fat index (P = 0.39). CONCLUSIONS The administration of insulin glargine therapy reduced the liver fat burden in patients with type 2 diabetes. However, the improvements in the liver fat fraction and glycemia control were not significantly different from those in the liraglutide group.

[18F]-fluorodeoxyglucose positron emission tomography of the cat brain: a feasibility study to investigate osteoarthritis-associated pain

The objective of this pilot study was to investigate central nervous system (CNS) changes related to osteoarthritis (OA)-associated chronic pain in cats using [(18)F]-fluorodeoxyglucose ((18)FDG) positron emission tomography (PET) imaging. The brains of five normal, healthy (non-OA) cats and seven cats with pain associated with naturally occurring OA were imaged using (18)FDG-PET during a standardized mild anesthesia protocol. The PET images were co-registered over a magnetic resonance image of a cat brain segmented into several regions of interest. Brain metabolism was assessed in these regions using standardized uptake values. The brain metabolism in the secondary somatosensory cortex, thalamus and periaqueductal gray matter was increased significantly (P ≤ 0.005) in OA cats compared with non-OA cats. This study indicates that (18)FDG-PET brain imaging in cats is feasible to investigate CNS changes related to chronic pain. The results also suggest that OA is associated with sustained nociceptive inputs and increased activity of the descending modulatory pathways.

Deep Learning: A Primer for Radiologists

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. ©RSNA, 2017.

MRI-determined liver proton density fat fraction, with MRS validation: Comparison of regions of interest sampling methods in patients with type 2 diabetes.

To assess the agreement between published magnetic resonance imaging (MRI)‐based regions of interest (ROI) sampling methods using liver mean proton density fat fraction (PDFF) as the reference standard.

Learning normalized inputs for iterative estimation in medical image segmentation

HighlightsImage segmentation pipeline based on Fully Convolutional Networks (FCN) and ResNets is proposed.FCN can serve as a pre‐processor to normalize medical imaging input data.A trainable FCN is an alternative to hand‐designed, modality specific pre‐processing steps.Our pipeline obtains or matches state‐of‐the‐art performance on 3 segmentation datasets. Graphical abstract Figure. No Caption available. Abstract In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC‐ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importance of a trainable pre‐processing when using FC‐ResNets and we show that a low‐capacity FCN model can serve as a pre‐processor to normalize medical input data. In our image segmentation pipeline, we use FCNs to obtain normalized images, which are then iteratively refined by means of a FC‐ResNet to generate a segmentation prediction. As in other fully convolutional approaches, our pipeline can be used off‐the‐shelf on different image modalities. We show that using this pipeline, we exhibit state‐of‐the‐art performance on the challenging Electron Microscopy benchmark, when compared to other 2D methods. We improve segmentation results on CT images of liver lesions, when contrasting with standard FCN methods. Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods. The obtained results illustrate the strong potential and versatility of the pipeline by achieving accurate segmentations on a variety of image modalities and different anatomical regions.

Validation of a Semiautomated Liver Segmentation Method Using CT for Accurate Volumetry

RATIONALE AND OBJECTIVES To compare the repeatability and agreement of a semiautomated liver segmentation method with manual segmentation for assessment of total liver volume on CT (computed tomography). MATERIALS AND METHODS This retrospective, institutional review board-approved study was conducted in 41 subjects who underwent liver CT for preoperative planning. The major pathologies encountered were colorectal cancer metastases, benign liver lesions and hepatocellular carcinoma. This semiautomated segmentation method is based on variational interpolation and 3D minimal path-surface segmentation. Total and subsegmental liver volumes were segmented from contrast-enhanced CT images in venous phase. Two image analysts independently performed semiautomated segmentations and two other image analysts performed manual segmentations. Repeatability and agreement of both methods were evaluated with intraclass correlation coefficients (ICC) and Bland-Altman analysis. Interaction time was recorded for both methods. RESULTS Bland-Altman analysis revealed an intrareader agreement of -1 ± 27 mL (mean ± 1.96 standard deviation) with ICC of 0.999 (P < .001) for manual segmentation and 12 ± 97 mL with ICC of 0.991 (P < .001) for semiautomated segmentation. Bland-Altman analysis revealed an interreader agreement of -4 ± 22 mL with ICC of 0.999 (P < .001) for manual segmentation and 5 ± 98 mL with ICC of 0.991 (P < .001) for semiautomated segmentation. Intermethod agreement was found to be 3 ± 120 mL with ICC of 0.988 (P < .001). Mean interaction time was 34.3 ± 16.7 minutes for the manual method and 8.0 ± 1.2 minutes for the semiautomated method (P < .001). CONCLUSIONS A semiautomated segmentation method can substantially shorten interaction time while preserving a high repeatability and agreement with manual segmentation.

Liver Segmentation on CT and MR Using Laplacian Mesh Optimization

Objective: The purpose of this paper is to describe a semiautomated segmentation method for the liver and evaluate its performance on CT-scan and MR images. Methods: First, an approximate 3-D model of the liver is initialized from a few user-generated contours to globally outline the liver shape. The model is then automatically deformed by a Laplacian mesh optimization scheme until it precisely delineates the patients liver. A correction tool was implemented to allow the user to improve the segmentation until satisfaction. Results: The proposed method was tested against 30 CT-scans from the SLIVER07 challenge repository and 20 MR studies from the Montreal University Hospital Center, covering a wide spectrum of liver morphologies and pathologies. The average volumetric overlap error was 5.1% for CT and 7.6% for MRI and the average segmentation time was 6 min. Conclusion: The obtained results show that the proposed method is efficient, reliable, and could effectively be used routinely in the clinical setting. Significance: The proposed approach can alleviate the cumbersome and tedious process of slice-wise segmentation required for precise hepatic volumetry, virtual surgery, and treatment planning.

Liver segmentation: indications, techniques and future directions

AbstractObjectivesLiver volumetry has emerged as an important tool in clinical practice. Liver volume is assessed primarily via organ segmentation of computed tomography (CT) and magnetic resonance imaging (MRI) images. The goal of this paper is to provide an accessible overview of liver segmentation targeted at radiologists and other healthcare professionals.MethodsUsing images from CT and MRI, this paper reviews the indications for liver segmentation, technical approaches used in segmentation software and the developing roles of liver segmentation in clinical practice.ResultsLiver segmentation for volumetric assessment is indicated prior to major hepatectomy, portal vein embolisation, associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) and transplant. Segmentation software can be categorised according to amount of user input involved: manual, semi-automated and fully automated. Manual segmentation is considered the “gold standard” in clinical practice and research, but is tedious and time-consuming. Increasingly automated segmentation approaches are more robust, but may suffer from certain segmentation pitfalls. Emerging applications of segmentation include surgical planning and integration with MRI-based biomarkers.ConclusionsLiver segmentation has multiple clinical applications and is expanding in scope. Clinicians can employ semi-automated or fully automated segmentation options to more efficiently integrate volumetry into clinical practice.Teaching points• Liver volume is assessed via organ segmentation on CT and MRI examinations. • Liver segmentation is used for volume assessment prior to major hepatic procedures. • Segmentation approaches may be categorised according to the amount of user input involved. • Emerging applications include surgical planning and integration with MRI-based biomarkers.

Kidney segmentation from a single prior shape in MRI

This paper reports a novel approach to 3D kidney segmentation from a single prior shape in magnetic resonance imaging (MRI) datasets. The proposed method is based on a hierarchic surface deformation algorithm, to generate a pre-personalized model, followed by an anamorphing segmentation algorithm, to extract the kidney capsule. Accuracy and precision are assessed by comparing our method over 20 kidney reconstructions segmented manually by 3 different observers on native MRI images. The experimental results show a volumetric overlap error of 6.39±2.47%, a relative volume difference of 1.87±1.39%, an average symmetric surface distance of 0.80±0.23mm, a root mean squared symmetric distance of 1.03±0.33mm and a maximum symmetric surface distance of 4.18±3.45mm. With our method, the capsules of both kidneys are segment in less than 40 seconds.