A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning

Objective: Manual contouring and registration for radiotherapy treatment planning and online adaptation for cervical cancer radiation therapy in computed tomography (CT) and magnetic resonance images (MRI) are often necessary. However manual intervention is time consuming and may suffer from inter or intra-rater variability. In recent years a number of computer-guided automatic or semi-automatic segmentation and registration methods have been proposed. Segmentation and registration in CT and MRI for this purpose is a challenging task due to soft tissue deformation, inter-patient shape and appearance variation and anatomical changes over the course of treatment. The objective of this work is to provide a state-of-the-art review of computer-aided methods developed for adaptive treatment planning and radiation therapy planning for cervical cancer radiation therapy. Methods: Segmentation and registration methods published with the goal of cervical cancer treatment planning and adaptation have been identified from the literature (PubMed and Google Scholar). A comprehensive description of each method is provided. Similarities and differences of these methods are highlighted and the strengths and weaknesses of these methods are discussed. A discussion about choice of an appropriate method for a given modality is provided. Results: In the reviewed papers a Dice similarity coefficient of around 0.85 along with mean absolute surface distance of 2-4. mm for the clinically treated volume were reported for transfer of contours from planning day to the treatment day. Conclusions: Most segmentation and non-rigid registration methods have been primarily designed for adaptive re-planning for the transfer of contours from planning day to the treatment day. The use of shape priors significantly improved segmentation and registration accuracy compared to other models

Machine learning and disease prediction in obstetrics

Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice

A survey of deep learning models in medical therapeutic areas.

pre-print1407 K

Improve definition of titanium tandems in MR-guided high dose rate brachytherapy for cervical cancer using proton density weighted MRI

BACKGROUND: For cervical cancer patients treated with MR-guided high dose rate brachytherapy, the accuracy of radiation delivery depends on accurate localization of both tumors and the applicator, e.g. tandem and ovoid. Standard T2-weighted (T2W) MRI has good tumor-tissue contrast. However, it suffers from poor uterus-tandem contrast, which makes the tandem delineation very challenging. In this study, we evaluated the possibility of using proton density weighted (PDW) MRI to improve the definition of titanium tandems. METHODS: Both T2W and PDW MRI images were obtained from each cervical cancer patient. Imaging parameters were kept the same between the T2W and PDW sequences for each patient except the echo time (90 ms for T2W and 5.5 ms for PDW) and the slice thickness (0.5 cm for T2W and 0.25 cm for PDW). Uterus-tandem contrast was calculated by the equation C = (S(u)-S(t))/S(u), where S(u) and S(t) represented the average signal in the uterus and the tandem, respectively. The diameter of the tandem was measured 1.5 cm away from the tip of the tandem. The tandem was segmented by the histogram thresholding technique. RESULTS: PDW MRI could significantly improve the uterus-tandem contrast compared to T2W MRI (0.42±0.24 for T2W MRI, 0.77±0.14 for PDW MRI, p=0.0002). The average difference between the measured and physical diameters of the tandem was reduced from 0.20±0.15 cm by using T2W MRI to 0.10±0.11 cm by using PDW MRI (p=0.0003). The tandem segmented from the PDW image looked more uniform and complete compared to that from the T2W image. CONCLUSIONS: Compared to the standard T2W MRI, PDW MRI has better uterus-tandem contrast. The information provided by PDW MRI is complementary to those provided by T2W MRI. Therefore, we recommend adding PDW MRI to the simulation protocol to assist tandem delineation process for cervical cancer patients