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

3D Ultrasound Strain Imaging of Puborectalis Muscle

The female pelvic floor (PF) muscles provide support to the pelvic organs. During delivery, some of these muscles have to stretch up to three times their original length to allow passage of the baby, leading frequently to damage and consequently later-life PF dysfunction (PFD). Three-dimensional (3D) ultrasound (US) imaging can be used to image these muscles and to diagnose the damage by assessing quantitative, geometric and functional information of the muscles through strain imaging. In this study we developed 3D US strain imaging of the PF muscles and explored its application to the puborectalis muscle (PRM), which is one of the major PF muscles.

Feasibility of a deep learning-based method for automated localization of pelvic floor landmarks using stress MR images

INTRODUCTION AND HYPOTHESIS

Magnetic resonance imaging (MRI) plays an important role in assessing pelvic organ prolapse (POP), and automated pelvic floor landmark localization potentially accelerates MRI-based measurements of POP. Herein, we aimed to develop and evaluate a deep learning-based technique for automated localization of POP-related landmarks.

METHODS

Ninety-six mid-sagittal stress MR images (at rest and at maximal Valsalva) were used for deep-learning model training and generalization testing. We randomly split our dataset into a training set of 73 images and a testing set of 23 images. One soft-tissue landmark (the cervical os [P1]) and three bony landmarks (the mid-pubic line [MPL] endpoints [P2&P3] and the sacrococcygeal inferior-pubic point [SCIPP] line endpoints [P3&P4]) were annotated by experts. We used an encoder-decoder structure to develop the deep learning model for automated localization of the four landmarks. Localization performance was assessed using the root square error (RSE), whereas the reference lines were assessed based on the length and orientation differences.

RESULTS

We localized landmarks (P1 to P4) with mean RSEs of 1.9 mm, 1.3 mm, 0.9 mm, and 3.6 mm. The mean length errors of the MPL and SCIPP line were 0.1 and -2.1 mm, and the mean orientation errors of the MPL and SCIPP line were -0.7° and -0.3°. Our method predicted each image in 0.015 s.

CONCLUSIONS

We demonstrated the feasibility of a deep learning-based approach for accurate and fast fully automated localization of bony and soft-tissue landmarks. This sped up the MR interpretation process for fast POP screening and treatment planning.

Classification of Liver Diseases Based on Ultrasound Image Texture Features

This paper discusses using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess based on ultrasound image texture features and a support vector machine (SVM) classifier. Among 79 cases of liver diseases including 44 cases of liver cancer and 35 cases of liver abscess, this research extracts 96 features including 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM) from the regions of interest (ROIs) in ultrasound images. Three feature selection models—(i) sequential forward selection (SFS), (ii) sequential backward selection (SBS), and (iii) F-score—are adopted to distinguish the two liver diseases. Finally, the developed system can classify liver cancer and liver abscess by SVM with an accuracy of 88.875%. The proposed methods for CAD can provide diagnostic assistance while distinguishing these two types of liver lesions.

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. 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. In the proposed method, organs are first tracked and segmented using particle filters and [Formula: see text]-means clustering with prior information. Then, the trajectories of the pelvic organs are modeled using a coupled switched hidden Markov model to classify the severity of POP. Results demonstrate that the presented method can automatically track and segment pelvic organs with a Dice similarity index above 78% and Hausdorff distance of [Formula: see text] for 94 tested cases while demonstrating correlation between organ movement and POP. This work aims to enable automatic tracking and analysis of multiple deformable structures from images to improve understanding of medical disorders.

The 3D Pelvic Inclination Correction System (PICS): A universally applicable coordinate system for isovolumetric imaging measurements, tested in women with pelvic organ prolapse (POP)

In pelvic organ prolapse (POP), the organs are pushed downward along the lines of gravity, so measurements along this longitudinal body axis are desirable. We propose a universally applicable 3D coordinate system that corrects for changes in pelvic inclination and that allows the localization of any point in the pelvis at rest or under dynamic conditions on magnetic resonance images (MRI) of pelvic floor disorders in a scanner- and software independent manner. The proposed 3D coordinate system called 3D Pelvic Inclination Correction System (PICS) is constructed utilizing four bony landmark points, with the origin set at the inferior pubic point, and three additional points at the sacrum (sacrococcygeal joint) and both ischial spines, which are clearly visible on MRI images. The feasibility and applicability of the moving frame was evaluated using MRI datasets from five women with pelvic organ prolapse, three undergoing static MRI and two undergoing dynamic MRI of the pelvic floor in a supine position. The construction of the coordinate system was performed utilizing the selected landmarks, with an initial implementation completed in MATLAB. In all cases the selected landmarks were clearly visible, with the construction of the 3D PICS and measurement of pelvic organ positions performed without difficulty. The resulting distance from the organ position to the horizontal PICS plane was compared to a traditional measure based on standard measurements in 2D slices. The two approaches demonstrated good agreement in each of the cases. The developed approach makes quantitative assessment of pelvic organ position in a physiologically relevant 3D coordinate system possible independent of pelvic movement relative to the scanner. It allows the accurate study of the physiologic range of organ location along the body axis ("up or down") as well as defects of the pelvic sidewall or birth-related pelvic floor injuries outside the midsagittal plane, not possible before in a 2D reference line system. Measures in 3D can be monitored over time and may reveal pathology before bothersome symptoms appear, as well as allowing comparison of outcomes between different patient pools after different surgical approaches.

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. Dynamic MRI is used for assessing POP and to complement clinical examination. Studies have shown some evidence on the association between the shape of the sacral curve and the development of POP. However, the sacral curve is currently extracted manually limiting studies to small datasets and inconclusive evidence. A method composed of an adaptive shortest path algorithm that enhances edge detection and linking, and an improved curve fitting procedure is proposed to automate the identification and segmentation of the sacral curve on MRI. The proposed method uses predetermined pixels surrounding the sacral curve that are found through edge detection to decrease computation time compared to other model-based segmentation algorithms. Moreover, the proposed method is fully automatic and does not require user input or training. Experimental results show that the proposed method can accurately identify sacral curves for nearly 91% of dynamic MRI cases tested in this study. The proposed model is robust and can be used to effectively identify bone structures on MRI.

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. However, bone structures are not easily differentiable from soft tissue on MRI as their pixel intensities tend to be very similar. In this paper, we present a model that combines support vector machines and nonlinear regression capturing global and local information to automatically identify the bounding boxes of bone structures on MRI. The model identifies the location of the pelvic bone structures by establishing the association between their relative locations and using local information such as texture features. Results show that the proposed method is able to locate the bone structures of interest accurately (dice similarity index >0.75) in 87-91% of the images. This research aims to enable accurate, consistent, and fully automated localization of bone structures on MRI to facilitate and improve the diagnosis of health conditions such as female POP.

Quantitative assessment of new MRI-based measurements to differentiate low and high stages of pelvic organ prolapse using support vector machines

INTRODUCTION AND HYPOTHESIS

The objective of this study was to quantitatively assess the ability of new MRI-based measurements to differentiate low and high stages of pelvic organ prolapse. New measurements representing pelvic structural characteristics are proposed and analyzed using support vector machines (SVM).

METHODS

This retrospective study used data from 207 women with different types and stages of prolapse. Their demographic information, clinical history, and dynamic MRI data were obtained from the database. New MRI measurements were extracted and analyzed based on these reference lines: pubococcygeal line (PCL), mid-pubic line (MPL), true conjugate line (TCL), obstetric conjugate line (OCL), and diagonal conjugate line (DCL). A classification model using SVM was designed to assess the impact of the features (variables) in classifying prolapse into low or high stage.

RESULTS

The classification model using SVM can accurately identified anterior prolapse with very high accuracy (>0.90), and apical and posterior prolapse with good accuracy (0.80 - 0.90). Two newly proposed MRI-based features were found to be significant in the identification of anterior and posterior prolapse: the angle between TCL and MPL for anterior prolapse, and the angle between DCL and PCL for posterior prolapse. The overall accuracy of posterior prolapse identification increased from 47% to 80% when the newly proposed MRI-based features were taken into consideration.

CONCLUSIONS

The proposed MRI-based measurements are effective in differentiating low and high stages of pelvic organ prolapse, particularly for posterior prolapse.

Fully 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 currently identified manually on MRI to identify 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 without any user interaction. However, bone structures are not easily differentiable from soft tissue on MRI as their pixel intensities tend to be very similar. In this research, we present a model that automatically identifies the bounding boxes of the bone structures on MRI using support vector machines (SVM) based classification and non-linear regression model that captures global and local information. Based on the relative locations of pelvic bones and organs, and local information such as texture features, the model identifies the location of the pelvic bone structures by establishing the association between their locations. Results show that the proposed method is able to locate the bone structures of interest accurately. The pubic bone, sacral promontory, and coccyx were correctly detected (DSI > 0.75) in 92%, 90%, and 88% of the testing images. This research aims to enable accurate, consistent and fully automated identification of pelvic bone structures on MRI to facilitate and improve the diagnosis of female pelvic organ prolapse.