Sinan Onal

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

Pelvic organ prolapse (POP) is a major womens 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. However, it is currently performed manually, making it a time-consuming and subjective procedure. We present a new segmentation approach for automating pelvic bone point identification on MRI. It consists of a multistage mechanism based on texture-based block classification, leak detection, and prior shape information. Texture-based block classification and clustering analysis using K-means algorithm are integrated to generate the initial bone segmentation and to identify leak areas. Prior shape information is incorporated to obtain the final bone segmentation. Then, the reference points are identified using morphological skeleton operation. Results demonstrate that the proposed method achieves higher bone segmentation accuracy compared to other segmentation methods. The proposed method can also automatically identify reference points faster and with more consistency compared with the manually identified point process by experts. This research aims to enable faster and consistent pelvic measurements on MRI to facilitate and improve the diagnosis of female POP.

Assessment of a semiautomated pelvic floor measurement model for evaluating pelvic organ prolapse on MRI

Introduction and hypothesisThe objective of this study was to assess the performance of a semiautomated pelvic floor measurement algorithmic model on dynamic magnetic resonance imaging (MRI) images compared with manual pelvic floor measurements for pelvic organ prolapse (POP) evaluation.MethodsWe examined 15 MRIs along the midsagittal view. Five reference points used for pelvic floor measurements were identified both manually and using our semiautomated measurement model. The two processes were compared in terms of accuracy and precision.ResultsThe semiautomated pelvic floor measurement model provided highly consistent and accurate locations for all reference points on MRI. Results also showed that the model can identify the reference points faster than the manual-point identification process.ConclusionThe semiautomated pelvic floor measurement model can be used to facilitate and improve the process of pelvic floor measurements on MRI. This will enable high throughput analysis of MRI data to improve the correlation analysis with clinical outcomes and potentially improve POP assessment.

MRI-based semi-automatic pelvimetry measurement for pelvic organ prolapse diagnosis

Magnetic resonance imaging (MRI) pelvimetry measurements are useful in the diagnosis of pelvic organ prolapse given the inaccuracy of clinical examination. However, MRI measurements are currently performed manually and can be inconsistent, time-consuming and inaccurate. In this paper, we present a scheme for semi-automatic measurements on MR images based on multi scale wavelet analysis. The experiments on the MR images show that the presented scheme can detect the points of reference on the pelvic bone structure to determine the lines needed for the assessment of pelvic organ prolapse. This may lead towards more accurate and faster pelvic organ prolapse diagnosis on dynamic MR studies, and possible screening procedures for predicting predisposition to pelvic organ prolapse by radiologic evaluation of pelvimetry measurements.

Automatic vertebra segmentation on dynamic magnetic resonance imaging

Abstract. 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.

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 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.

Interior point search for nonparametric image segmentation

Precise object boundary detection for automatic image segmentation is critical for image analysis, including that used in computer-aided diagnosis. However, such detection traditionally uses active contour or snake models requiring accurate initialization and parameter optimization. Identifying optimal parameter values requires time-consuming multiple runs and provides results that vary by user expertise, limiting the use of these models in high-throughput or real-time situations. Thus, we developed a nonparametric snake model using an interior point search method applied in iterations to find and improve the set of snake points forming the edge of a shape. At each iteration, one or more snake points are replaced by others in the edge map. We validated the model using binary and continuous edge images of single and multiple objects, and noisy and real images, comparing the results to those obtained using traditional snake models. The proposed model not only provides better results on all image types tested but is more robust than traditional snake models. Unlike traditional snake models, the proposed model requires no user interaction for initializing snakes and no preprocessing of noisy images. Thus, our method offers robust automatic image segmentation that is simpler to use and less time-consuming than traditional snake models.

Segmentation of sacral curve on dynamic MRI for diagnosis of pelvic organ prolapse

Pelvic organ prolapse (POP) is a critical health condition for women. Dynamic magnetic resonance imaging (MRI) is currently used for assessing POP and to complement clinical examination. Current 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 resulting in a time-consuming and subjective process. A new method is proposed to automate the identification and segmentation of the sacral curve on MRI. The proposed method identifies the region of interest without any user input by using our previously developed pelvic floor point identification model. Edges of the sacral structure are detected to identify points along the curve, which are then connected using a proposed adaptive shortest path algorithm. These points are used to finalize the segmentation of the sacral curve using smoothing curve fitting algorithm. Results show that the proposed method can achieve good accuracy for 80% of the dataset used in this study.

Automated and simultaneous fovea center localization and macula segmentation using the new dynamic identification and classification of edges model

Abstract. Detecting the position of retinal structures, including the fovea center and macula, in retinal images plays a key role in diagnosing eye diseases such as optic nerve hypoplasia, amblyopia, diabetic retinopathy, and macular edema. However, current detection methods are unreliable for infants or certain ethnic populations. Thus, a methodology is proposed here that may be useful for infants and across ethnicities that automatically localizes the fovea center and segments the macula on digital fundus images. First, dark structures and bright artifacts are removed from the input image using preprocessing operations, and the resulting image is transformed to polar space. Second, the fovea center is identified, and the macula region is segmented using the proposed dynamic identification and classification of edges (DICE) model. The performance of the method was evaluated using 1200 fundus images obtained from the relatively large, diverse, and publicly available Messidor database. In 96.1% of these 1200 cases, the distance between the fovea center identified manually by ophthalmologists and automatically using the proposed method remained within 0 to 8 pixels. The dice similarity index comparing the manually obtained results with those of the model for macula segmentation was 96.12% for these 1200 cases. Thus, the proposed method displayed a high degree of accuracy. The methodology using the DICE model is unique and advantageous over previously reported methods because it simultaneously determines the fovea center and segments the macula region without using any structural information, such as optic disc or blood vessel location, and it may prove useful for all populations, including infants.

Design of a Universal Laparoscopic Suturing Device

Minimally invasive surgery (MIS) or laparoscopic surgery has changed the focus of surgery and has become an alternative to open surgical procedures. Operations are performed through small incisions in the abdomen thus avoiding the need for large incisions. This results in less tissue trauma, less scarring, and faster post-operative recovery time. However, the inherent challenges of laparoscopic procedures include limited visibility, constrained working space and the need for advanced surgical tools to safely and efficiently perform the surgical procedure. It is also necessary for surgeons to obtain advanced surgical training to perform these procedures.Copyright