StAC-DA: Structure aware cross-modality domain adaptation framework with image and feature-level adaptation for medical image segmentation.

Objective: Convolutional neural networks (CNNs) have achieved state-of-the-art results in various medical image segmentation tasks. However, CNNs often assume that the source and target dataset follow the same probability distribution and when this assumption is not satisfied their performance degrades significantly. This poses a limitation in medical image analysis, where including information from different imaging modalities can bring large clinical benefits.

DistilIQA: Distilling Vision Transformers for no-reference perceptual CT image quality assessment.

No-reference image quality assessment (IQA) is a critical step in medical image analysis, with the objective of predicting perceptual image quality without the need for a pristine reference image. The application of no-reference IQA to CT scans is valuable in providing an automated and objective approach to assessing scan quality, optimizing radiation dose, and improving overall healthcare efficiency. In this paper, we introduce DistilIQA, a novel distilled Vision Transformer network designed for no-reference CT image quality assessment.

CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation.

Domain Adaptation (DA) has recently been of strong interest in the medical imaging community. While a large variety of DA techniques have been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems.

Estimated Levator Ani Subtended Volume: A Predictive Biomarker for Surgical Outcomes Following Native Tissue Apical Repair.

Objective: The aim of the study was to investigate the clinical utility of estimated levator ani subtended volume (eLASV) as a prospective preoperative biomarker for prediction of surgical outcomes.

Study Design: This is a prospective case-control pilot study. Patients were recruited and gave consent between January 2018 and December 2020.

EMONAS-Net: Efficient multiobjective neural architecture search using surrogate-assisted evolutionary algorithm for 3D medical image segmentation.

Deep learning plays a critical role in medical image segmentation. Nevertheless, manually designing a neural network for a specific segmentation problem is a very difficult and time-consuming task due to the massive hyperparameter search space, long training time and large volumetric data. Therefore, most designed networks are highly complex, task specific and over-parametrized.

AdaEn-Net: An ensemble of adaptive 2D-3D Fully Convolutional Networks for medical image segmentation.

Fully Convolutional Networks (FCNs) have emerged as powerful segmentation models but are usually designed manually, which requires extensive time and can result in large and complex architectures. There is a growing interest to automatically design efficient architectures that can accurately segment 3D medical images. However, most approaches either do not fully exploit volumetric information or do not optimize the model's size.

Cost-effectiveness of a preoperative pelvic MRI in pelvic organ prolapse surgery.

Objective: To investigate the cost-effectiveness of preoperative pelvic magnetic resonance imaging (MRI) in identifying women at high risk of surgical failure following apical repair for pelvic organ prolapse (POP).

Methods: A decision tree (TreeAgePro Healthcare software) was designed to compare outcomes and costs of screening with a pelvic MRI versus no screening. For the strategy with MRI, expected surgical outcomes were based on a calculated value of the estimated levator ani subtended volume (eLASV) from previously published work.

Automated contour tracking and trajectory classification of pelvic organs on dynamic MRI.

A method is presented to automatically track and segment pelvic organs on dynamic magnetic resonance imaging (MRI) followed by multiple-object trajectory classification to improve understanding of pelvic organ prolapse (POP). POP is a major health problem in women where pelvic floor organs fall from their normal position and bulge into the vagina. Dynamic MRI is presently used to analyze the organs' movements, providing complementary support for clinical examination.

Automatic vertebra segmentation on dynamic magnetic resonance imaging.

The automatic extraction of the vertebra's shape from dynamic magnetic resonance imaging (MRI) could improve understanding of clinical conditions and their diagnosis. It is hypothesized that the shape of the sacral curve is related to the development of some gynecological conditions such as pelvic organ prolapse (POP). POP is a critical health condition for women and consists of pelvic organs dropping from their normal position.

Automated tracking, segmentation and trajectory classification of pelvic organs on dynamic MRI.

Pelvic organ prolapse is a major health problem in women where pelvic floor organs (bladder, uterus, small bowel, and rectum) fall from their normal position and bulge into the vagina. Dynamic Magnetic Resonance Imaging (DMRI) is presently used to analyze the organs' movements from rest to maximum strain providing complementary support for diagnosis. However, there is currently no automated or quantitative approach to measure the movement of the pelvic organs and their correlation with the severity of prolapse.

A web-based knowledge management system integrating Western and Traditional Chinese Medicine for relational medical diagnosis.

This article presents the design of a web-based knowledge management system as a training and research tool for the exploration of key relationships between Western and Traditional Chinese Medicine, in order to facilitate relational medical diagnosis integrating these mainstream healing modalities. The main goal of this system is to facilitate decision-making processes, while developing skills and creating new medical knowledge. Traditional Chinese Medicine can be considered as an ancient relational knowledge-based approach, focusing on balancing interrelated human functions to reach a healthy state.

Automated Localization of Multiple Pelvic Bone Structures on MRI.

In this paper, we present a fully automated localization method for multiple pelvic bone structures on magnetic resonance images (MRI). Pelvic bone structures are at present identified manually on MRI to locate reference points for measurement and evaluation of pelvic organ prolapse (POP). Given that this is a time-consuming and subjective procedure, there is a need to localize pelvic bone structures automatically.

MRI-based segmentation of pubic bone for evaluation of pelvic organ prolapse.

Pelvic organ prolapse (POP) is a major women's health problem. Its diagnosis through magnetic resonance imaging (MRI) has become popular due to current inaccuracies of clinical examination. The diagnosis of POP on MRI consists of identifying reference points on pelvic bone structures for measurement and evaluation.

Synergetic effects of doxycycline-loaded chitosan nanoparticles for improving drug delivery and efficacy.

Introduction: Doxycycline, a broad-spectrum antibiotic, is the most commonly prescribed antibiotic worldwide for treating infectious diseases. It may be delivered orally or intravenously but can lead to gastrointestinal irritation and local inflammation. For treatment of uterine infections, transcervical administration of doxycycline encapsulated in nanoparticles made of biodegradable chitosan may improve sustained delivery of the drug, thereby minimizing adverse effects and improving drug efficacy.