Placenta Accreta Spectrum and Hysterectomy Prediction Using MRI Radiomic Features.

In women with placenta accreta spectrum (PAS), patient management may involve cesarean hysterectomy at delivery. Magnetic resonance imaging (MRI) has been used for further evaluation of PAS and surgical planning. This work tackles two prediction problems: predicting presence of PAS and predicting hysterectomy using MR images of pregnant patients.

CascadeNet for hysterectomy prediction in pregnant women due to placenta accreta spectrum.

In severe cases, placenta accreta spectrum (PAS) requires emergency hysterectomy, endangering the life of both mother and fetus. Early prediction may reduce complications and aid in management decisions in these high-risk pregnancies. In this work, we developed a novel convolutional network architecture to combine MRI volumes, radiomic features, and custom feature maps to predict PAS severe enough to result in hysterectomy after fetal delivery in pregnant women.

Automatic Segmentation of Uterine Cavity and Placenta on MR Images Using Deep Learning.

Magnetic resonance imaging (MRI) is useful for the detection of abnormalities affecting maternal and fetal health. In this study, we used a fully convolutional neural network for simultaneous segmentation of the uterine cavity and placenta on MR images. We trained the network with MR images of 181 patients, with 157 for training and 24 for validation.

LED-based Hyperspectral Endoscopic Imaging.

Hyperspectral endoscopy can offer multiple advantages as compared to conventional endoscopy. Our goal is to design and develop a real-time hyperspectral endoscopic imaging system for the diagnosis of gastrointestinal (GI) tract cancers using a micro-LED array as an in-situ illumination source. The wavelengths of the system range from ultraviolet to visible and near infrared.

Identifying Unique Acoustic Signatures from Chemically-Crosslinked Microbubble Clusters Using Deep Learning.

Ultrasound contrast agents (UCA) are gas encapsulated microspheres that oscillate volumetrically when exposed to an ultrasound field producing a backscattered signal which can be used for improved ultrasound imaging and drug delivery. UCA's are being used widely for contrast-enhanced ultrasound imaging, but there is a need for improved UCAs to develop faster and more accurate contrast agent detection algorithms. Recently, we introduced a new class of lipid based UCAs called Chemically Cross-linked Microbubble Clusters (CCMCs).

A low-cost PVC-based dual-modality kidney phantom.

Phantoms are invaluable tools broadly used for research and training purposes designed to mimic tissues and structures in the body. In this paper, polyvinyl chloride (PVC)-plasticizer and silicone rubbers were explored as economical materials to reliably create long-lasting, realistic kidney phantoms with contrast under both ultrasound (US) and X-ray imaging. The radiodensity properties of varying formulations of soft PVC-based gels were characterized to allow adjustable image intensity and contrast.

Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed points.

Given the prevalence of cardiovascular diseases (CVDs), the segmentation of the heart on cardiac computed tomography (CT) remains of great importance. Manual segmentation is time-consuming and intra-and inter-observer variabilities yield inconsistent and inaccurate results. Computer-assisted, and in particular, deep learning approaches to segmentation continue to potentially offer an accurate, efficient alternative to manual segmentation.

Virtual Reality Assisted Cardiac Catheterization.

Cardiac catheterization is a delicate strategy often used during various heart procedures. However, the procedure carries a myriad of risks associated with it, including damage to the vessel or heart itself, blood clots, and arrhythmias. Many of these risks increase in probability as the length of the operation increases, creating a demand for a more accurate procedure while reducing the overall time required.

Technical note: The effect of image annotation with minimal manual interaction for semiautomatic prostate segmentation in CT images using fully convolutional neural networks.

Purpose: The goal is to study the performance improvement of a deep learning algorithm in three-dimensional (3D) image segmentation through incorporating minimal user interaction into a fully convolutional neural network (CNN).

Methods: A U-Net CNN was trained and tested for 3D prostate segmentation in computed tomography (CT) images. To improve the segmentation accuracy, the CNN's input images were annotated with a set of border landmarks to supervise the network for segmenting the prostate.

Deep learning-based segmentation of the placenta and uterus on MR images.

Magnetic resonance imaging has been recently used to examine the abnormalities of the placenta during pregnancy. Segmentation of the placenta and uterine cavity allows quantitative measures and further analyses of the organs. The objective of this study is to develop a segmentation method with minimal user interaction.

The Contribution of Thoracic Radiation Dose Volumes to Subsequent Development of Cardiovascular Disease in Cancer Survivors.

Background: Although radiation therapy (RT) has been recognized for contributing to cardiovascular disease (CVD), it is unknown whether specific doses received by cardiovascular tissues influence development.

Objective: In this pilot study, we examined the contribution of RT dose distribution on the development of CVD events in patients with cancer within 5 years of RT.

Methods: A retrospective case-controlled design was used matching 28 cases receiving thoracic RT who subsequently developed an adverse CVD event with 28 controls based upon age, gender, and cancer type.

Development of a real-time spectral imaging system using in-site micro-LED-based illumination and high-speed micro-camera for endoscopic applications.

We designed a compact, real-time LED-based endoscopic imaging system for the detection of various diseases including cancer. In gastrointestinal applications, conventional endoscopy cannot reliably differentiate tumor from normal tissue. Current hyperspectral imaging systems are too slow to be used for real-time endoscopic applications.

Assessing reproducibility in Magnetic Resonance (MR) Radiomics features between Deep-Learning segmented and Expert Manual segmented data and evaluating their diagnostic performance in Pregnant Women with suspected Placenta Accreta Spectrum (PAS).

A Deep-Learning (DL) based segmentation tool was applied to a new magnetic resonance imaging dataset of pregnant women with suspected Placenta Accreta Spectrum (PAS). Radiomic features from DL segmentation were compared to those from expert manual segmentation via intraclass correlation coefficients (ICC) to assess reproducibility. An additional imaging marker quantifying the placental location within the uterus (PLU) was included.

Abdominal muscle segmentation from CT using a convolutional neural network.

CT is widely used for diagnosis and treatment of a variety of diseases, including characterization of muscle loss. In many cases, changes in muscle mass, particularly abdominal muscle, indicate how well a patient is responding to treatment. Therefore, physicians use CT to monitor changes in muscle mass throughout the patient's course of treatment.

Development of a new polarized hyperspectral imaging microscope.

In this study, we proposed and designed a transmission mode polarized hyperspectral imaging microscope (PHSIM). The hyperspectral imaging (HSI) component is based on the snapscan with a hyperspectral camera. The HSI wavelength range is from 467-700 nm.

Image Guided Mitral Valve Replacement: Registration of 3D Ultrasound and 2D X-ray Images.

Mitral valve repair or replacement is important in the treatment of mitral regurgitation. For valve replacement, a transcatheter approach had the possibility of decrease the invasiveness of the procedure while retaining the benefit of replacement over repair. However, fluoroscopy images acquired during the procedure provide no anatomical information regarding the placement of the probe tip once the catheter has entered a cardiac chamber.

Augmented Reality-Assisted Biopsy of Soft Tissue Lesions.

Guided biopsy of soft tissue lesions can be challenging in the presence of sensitive organs or when the lesion itself is small. Computed tomography (CT) is the most frequently used modality to target soft tissue lesions. In order to aid physicians, small field of view (FOV) low dose non-contrast CT volumes are acquired prior to intervention while the patient is on the procedure table to localize the lesion and plan the best approach.

Siamese neural networks for the classification of high-dimensional radiomic features.

This study demonstrates that a variant of a Siamese neural network architecture is more effective at classifying high-dimensional radiomic features (extracted from T2 MRI images) than traditional models, such as a Support Vector Machine or Discriminant Analysis. Ninety-nine female patients, between the ages of 20 and 48, were imaged with T2 MRI. Using biopsy pathology, the patients were separated into two groups: those with breast cancer (N=55) and those with GLM (N=44).

Development of a polarized hyperspectral microscope for cardiac fiber orientation imaging.

Myocardial fiber orientation is closely related to the functions of the heart. The development of imaging tools for depicting myocardial fiber orientation is important. We developed a polarized hyperspectral imaging microscope (PHSIM) for cardiac fiber orientation imaging, which is capable of polarimetric imaging and hyperspectral imaging.

Hyperspectral Microscopic Imaging for Automatic Detection of Head and Neck Squamous Cell Carcinoma Using Histologic Image and Machine Learning.

The purpose of this study is to develop hyperspectral imaging (HSI) for automatic detection of head and neck cancer cells on histologic slides. A compact hyperspectral microscopic system is developed in this study. Histologic slides from 15 patients with squamous cell carcinoma (SCC) of the larynx and hypopharynx are imaged with the system.

A complex dual-modality kidney phantom for renal biopsy studies.

We developed a reliable and repeatable process to create hyper-realistic, kidney phantoms with tunable image visibility under ultrasound (US) and CT imaging modalities. A methodology was defined to create phantoms that could be produced for renal biopsy evaluation. The final complex kidney phantom was devised containing critical structures of a kidney: kidney cortex, medulla, and ureter.

Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net.

Cardiac magnetic resonance (CMR) imaging is considered the standard imaging modality for volumetric analysis of the right ventricle (RV), an especially important practice in the evaluation of heart structure and function in patients with repaired Tetralogy of Fallot (rTOF). In clinical practice, however, this requires time-consuming manual delineation of the RV endocardium in multiple 2-dimensional (2D) slices at multiple phases of the cardiac cycle. In this work, we employed a U-Net based 2D convolutional neural network (CNN) classifier in the fully automatic segmentation of the RV blood pool.

Segmentation of uterus and placenta in MR images using a fully convolutional neural network.

Segmentation of the uterine cavity and placenta in fetal magnetic resonance (MR) imaging is useful for the detection of abnormalities that affect maternal and fetal health. In this study, we used a fully convolutional neural network for 3D segmentation of the uterine cavity and placenta while a minimal operator interaction was incorporated for training and testing the network. The user interaction guided the network to localize the placenta more accurately.

Incorporating minimal user input into deep learning based image segmentation.

Computer-assisted image segmentation techniques could help clinicians to perform the border delineation task faster with lower inter-observer variability. Recently, convolutional neural networks (CNNs) are widely used for automatic image segmentation. In this study, we used a technique to involve observer inputs for supervising CNNs to improve the accuracy of the segmentation performance.