Deep Learning Models Connecting Images and Text: A Primer for Radiologists.

In radiology practice, medical images are described and interpreted by radiologists in text reports. Recent technical developments enabling deep learning models to connect images and text may facilitate the radiologic workflow. These developments include advances in data embedding, self-supervised learning, zero-shot learning, and transformer-based model architectures.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients.

Automated Detection of Brain Metastases on T1-Weighted MRI Using a Convolutional Neural Network: Impact of Volume Aware Loss and Sampling Strategy.

Background: Detection of brain metastases (BM) and segmentation for treatment planning could be optimized with machine learning methods. Convolutional neural networks (CNNs) are promising, but their trade-offs between sensitivity and precision frequently lead to missing small lesions.

Hypothesis: Combining volume aware (VA) loss function and sampling strategy could improve BM detection sensitivity.

Deep Learning: An Update for Radiologists.

Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image features by which to classify data. Convolutional neural networks (CNNs), the core of deep learning methods for imaging, are multilayered artificial neural networks with weighted connections between neurons that are iteratively adjusted through repeated exposure to training data.

Learning normalized inputs for iterative estimation in medical image segmentation.

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.

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.

Liver segmentation: indications, techniques and future directions.

Objectives: Liver 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.

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 patient's liver.

Comparison of MRI- and CT-based semiautomated liver segmentation: a validation study.

Purpose: To compare the repeatability, agreement, and efficiency of MRI- and CT-based semiautomated liver segmentation for the assessment of total and subsegmental liver volume.

Methods: This retrospective study was conducted in 31 subjects who underwent contemporaneous liver MRI and CT. Total and subsegmental liver volumes were segmented from contrast-enhanced 3D gradient-recalled echo MRI sequences and CT images.

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

Purpose: 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.

Materials And Methods: This retrospective, internal review board-approved study was conducted in 35 patients with type 2 diabetes. Liver PDFF was measured by magnetic resonance spectroscopy (MRS) using a stimulated-echo acquisition mode sequence and MRI using a multiecho spoiled gradient-recalled echo sequence at 3.

[(18)F]-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.

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.

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.