Renal Cortex, Medulla and Pelvicaliceal System Segmentation on Arterial Phase CT Images with Random Patch-based Networks.

Renal segmentation on contrast-enhanced computed tomography (CT) provides distinct spatial context and morphology. Current studies for renal segmentations are highly dependent on manual efforts, which are time-consuming and tedious. Hence, developing an automatic framework for the segmentation of renal cortex, medulla and pelvicalyceal system is an important quantitative assessment of renal morphometry. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing. However, the segmentation of renal structures can be challenging due to the limited field-of-view (FOV) and variability among patients. In this paper, we propose a method to automatically label the renal cortex, the medulla and pelvicalyceal system. First, we retrieved 45 clinically-acquired deidentified arterial phase CT scans (45 patients, 90 kidneys) without diagnosis codes (ICD-9) involving kidney abnormalities. Second, an interpreter performed manual segmentation to pelvis, medulla and cortex slice-by-slice on all retrieved subjects under expert supervision. Finally, we proposed a patch-based deep neural networks to automatically segment renal structures. Compared to the automatic baseline algorithm (3D U-Net) and conventional hierarchical method (3D U-Net Hierarchy), our proposed method achieves improvement of 0.7968 to 0.6749 (3D U-Net), 0.7482 (3D U-Net Hierarchy) in terms of mean Dice scores across three classes (p-value < 0.001, paired t-tests between our method and 3D U-Net Hierarchy). In summary, the proposed algorithm provides a precise and efficient method for labeling renal structures.

Phase identification for dynamic CT enhancements with generative adversarial network.

PURPOSE: Dynamic contrast-enhanced computed tomography (CT) is widely used to provide dynamic tissue contrast for diagnostic investigation and vascular identification. However, the phase information of contrast injection is typically recorded manually by technicians, which introduces missing or mislabeling. Hence, imaging-based contrast phase identification is appealing, but challenging, due to large variations among different contrast protocols, vascular dynamics, and metabolism, especially for clinically acquired CT scans. The purpose of this study is to perform imaging-based phase identification for dynamic abdominal CT using a proposed adversarial learning framework across five representative contrast phases. METHODS: A generative adversarial network (GAN) is proposed as a disentangled representation learning model. To explicitly model different contrast phases, a low dimensional common representation and a class specific code are fused in the hidden layer. Then, the low dimensional features are reconstructed following a discriminator and classifier. 36 350 slices of CT scans from 400 subjects are used to evaluate the proposed method with fivefold cross-validation with splits on subjects. Then, 2216 slices images from 20 independent subjects are employed as independent testing data, which are evaluated using multiclass normalized confusion matrix. RESULTS: The proposed network significantly improved correspondence (0.93) over VGG, ResNet50, StarGAN, and 3DSE with accuracy scores 0.59, 0.62, 0.72, and 0.90, respectively (P < 0.001 Stuart-Maxwell test for normalized multiclass confusion matrix). CONCLUSION: We show that adversarial learning for discriminator can be benefit for capturing contrast information among phases. The proposed discriminator from the disentangled network achieves promising results.

From the Reading Room to the Courtroom-The Use of Molecular Radionuclide Imaging in Criminal Trials.

Recent years have witnessed an expanded use of single-photon emission CT and PET for a wide range of clinical applications, including imaging of brain abnormalities. As a result, molecular brain imaging is now being more extensively utilized in criminal cases, in particular in the sentencing phase of a trial. This perspective aims to provide a brief overview for the practicing radiologist of this expanded use of single-photon emission CT and PET in criminal cases and will discuss the role of radiology in this field.

Expert witness blinding strategies to mitigate bias in radiology malpractice cases: a comprehensive review of the literature.

Like all physicians, radiologists in the United States are subject to frequent and costly medical malpractice claims. Legal scholars and physicians concur that the US civil justice system is neither precise nor accurate in determining whether malpractice has truly occurred in cases in which claims are made. Sometimes, this inaccuracy is driven by biases inherent in medical expert-witness opinions. For example, expert-witness testimony involving "missed" radiology findings can be negatively affected by several cognitive biases, such as contextual bias, hindsight bias, and outcome bias. Biases inherent in the US legal system, such as selection bias, compensation bias, and affiliation bias, also play important roles. Fortunately, many of these biases can be significantly mitigated or eliminated through the use of appropriate blinding techniques. This paper reviews the major works on expert-witness blinding in the legal scholarship and the radiology professional literature. Its purpose is to acquaint the reader with the evidence that unblinded expert-witness testimony is tainted by multiple sources of bias and to examine proposed strategies for addressing these biases through blinding.

Hand sanitizer-dispensing door handles increase hand hygiene compliance: a pilot study.

Improving rates of hand hygiene compliance (HHC) has been shown to reduce nosocomial disease. We compared the HHC for a traditional wall-mounted unit and a novel sanitizer-dispensing door handle device in a hospital inpatient ultrasound area. HHC increased 24.5%-77.1% (P < .001) for the exam room with the sanitizer-dispensing door handle, whereas it remained unchanged for the other rooms. Technical improvements like a sanitizer-dispensing door handle can improve hospital HHC.