Features of tumor texture influence surgery and outcome in intracranial meningioma.

BACKGROUND: Texture-related factors such as consistency, vascularity, and adherence vary considerably in meningioma and are thought to be linked with surgical resectability and morbidity. However, data analyzing the true impact of meningioma texture on the surgical management is sparse. METHODS: Patients with intracranial meningioma treated between 08/2014 and 04/2018 at our institution were prospectively collected for demographics, clinical presentation, histology, and surgical treatment with related morbidity and extend of resection. Tumor characteristics were reported by the surgeon using a standardized questionnaire including items such as tumor consistency, homogeneity, vascularization, and adherence to surrounding neurovascular structure and analyzed for their impact surgical outcome parameters using univariate and logistic regression analyses. RESULTS: Tumor texture-related parameters of 300 patients (72.3% female) with meningioma were analyzed. Meningioma localizations were grouped into 3 different cohorts namely convexity, skull base, and posterior. Postoperative occurrence of a neurological deficit (transient 23.0%; permanent 6.1%) was associated with the duration of surgery (P = .001), size of tumor (P = .046), tumor vascularization (P = .015), and adherence to neurovascular structures (P = .002). Coherently, the duration of surgery (mean 230.99 ± 101.33 min) was associated with size of tumor (P < .0001), vascularization (P < .0001), and adherence (P < .0001). Similar associations were recapitulated in subgroup analyses of different tumor localizations. Noteworthy, tumor rigidity had no significant impact on time of surgery and neurological outcome. CONCLUSIONS: Our analysis demonstrates that tumor texture has an impact on the surgical management of meningioma and provides data that tumor vascularization and adherence are significant factors influencing surgical outcome whereas the influence of tumor consistency has less impact than previously thought.

Automatic model-based semantic registration of multimodal MRI knee data.

PURPOSE: To propose a robust and automated model-based semantic registration for the multimodal alignment of the knee bone and cartilage from three-dimensional (3D) MR image data. MATERIALS AND METHODS: The movement of the knee joint can be semantically interpreted as a combination of movements of each bone. A semantic registration of the knee joint was implemented by separately reconstructing the rigid movements of the three bones. The proposed method was validated by registering 3D morphological MR datasets of 25 subjects into the corresponding T2 map datasets, and was compared with rigid and elastic methods using two criteria: the spatial overlap of the manually segmented cartilage and the distance between the same landmarks in the reference and target datasets. RESULTS: The mean Dice Similarity Coefficient (DSC) of the overlapped cartilage segmentation was increased to 0.68 ± 0.1 (mean ± SD) and the landmark distance was reduced to 1.3 ± 0.3 mm after the proposed registration method. Both metrics were statistically superior to using rigid (DSC: 0.59 ± 0.12; landmark distance: 2.1 ± 0.4 mm) and elastic (DSC: 0.64 ± 0.11; landmark distance: 1.5 ± 0.5 mm) registrations. CONCLUSION: The proposed method is an efficient and robust approach for the automated registration between morphological knee datasets and T2 MRI relaxation maps.

Automatic detection of anatomical landmarks on the knee joint using MRI data.

PURPOSE: To propose a new automated learning-based scheme for locating anatomical landmarks on the knee joint using three-dimensional (3D) MR image data. MATERIALS AND METHODS: This method makes use of interest points as candidates for landmarks. All candidates are evaluated by a "coarse to fine" 3D feature descriptor computed from manually placed landmarks in training datasets. The results are refined using a multi-classifier boosting system. We demonstrate our method by the detection of 24 landmarks on the knee joint of 35 subjects. To verify the robustness, the test datasets differ in contrast, resolution, patient positioning, and health condition of the knee joint. The proposed method is evaluated by measuring the distance between manually placed landmarks and automatically detected landmarks and the computational cost for detecting one landmark in a 3D dataset. RESULTS: The results reveal that the method is capable of localizing landmarks with a reasonable accuracy (1.64 ± 1.03 mm [mean ± standard deviation]), sensitivity (97%) and run time efficiency (4.82 s). CONCLUSION: This study suggests that the proposed method is an accurate and robust approach for the automated landmark detection in various MR datasets. The proposed method can be used as the initialization or constraint in higher level medical image processing workflows such as in kinematic description, segmentation and registration.

Focused shape models for hip joint segmentation in 3D magnetic resonance images.

Deformable models incorporating shape priors have proved to be a successful approach in segmenting anatomical regions and specific structures in medical images. This paper introduces weighted shape priors for deformable models in the context of 3D magnetic resonance (MR) image segmentation of the bony elements of the human hip joint. The fully automated approach allows the focusing of the shape model energy to a priori selected anatomical structures or regions of clinical interest by preferentially ordering the shape representation (or eigen-modes) within this type of model to the highly weighted areas. This focused shape model improves accuracy of the shape constraints in those regions compared to standard approaches. The proposed method achieved femoral head and acetabular bone segmentation mean absolute surface distance errors of 0.55±0.18mm and 0.75±0.20mm respectively in 35 3D unilateral MR datasets from 25 subjects acquired at 3T with different limited field of views for individual bony components of the hip joint.

Automated bone segmentation from large field of view 3D MR images of the hip joint.

Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers. Image sequences included water-excitation dual echo stead state (FOV 38.6 × 24.1 cm, matrix 576 × 360, thickness 0.61 mm) in all subjects and multi-echo data image combination (FOV 37.6 × 23.5 cm, matrix 576 × 360, thickness 0.70 mm) for a subset of eight subjects. Following manual segmentation of femoral (head-neck, proximal-shaft) and innominate (ilium+ischium+pubis) bone, automated bone segmentation proceeded via two approaches: (1) multi-atlas segmentation incorporating non-rigid registration and (2) an advanced ASM-based scheme. Mean inter- and intra-rater reliability Dice's similarity coefficients (DSC) for manual segmentation of femoral and innominate bone were (0.970, 0.963) and (0.971, 0.965). Compared with manual data, mean DSC values for femoral and innominate bone volumes using automated multi-atlas and ASM-based methods were (0.950, 0.922) and (0.946, 0.917), respectively. Both approaches delivered accurate (high DSC values) segmentation results; notably, ASM data were generated in substantially less computational time (12 min versus 10 h). Both automated algorithms provided accurate 3D bone volumetric descriptions for MR-based measures in the hip region. The highly computational efficient ASM-based approach is more likely suitable for future clinical applications such as extracting bone-cartilage interfaces for potential cartilage segmentation.

Substitute voice production: quantification of PE segment vibrations using a biomechanical model.

After total larynx excision due to laryngeal cancer, the tracheoesophageal substitute tissue vibrations at the intersection between the pharynx and the esophagus [pharyngoesophageal segment (PE segment)] serve as voice generator. The quality of the substitute voice significantly depends on the vibratory characteristics of the PE segment. For improving voice rehabilitation, the relationship between the PE dynamics and the resulting substitute voice quality is a matter of particular interest. Precondition for a comprehensive analysis of this relationship is an objective quantification of the PE vibrations. For quantification purposes, a method is proposed, which is based on the reproduction of the tissue vibrations by means of a biomechanical model of the PE segment. An optimization procedure for an automatic determination of appropriate model parameters is suggested to adapt the model dynamics to tissue movements extracted from high-speed (HS) videos. The applicability of the optimization procedure is evaluated with ten synthetic data sets. A mean error of 8.2% for the determination of previously defined model parameters was achieved as well as an overall stability of 7.1%. The application of the model to six HS recordings presented a mean correlation of the vibration patterns of 82%.

Spatio-temporal quantification of vocal fold vibrations using high-speed videoendoscopy and a biomechanical model.

Pathologic changes within the organic constitution of vocal folds or a functional impairment of the larynx may result in disturbed or even irregular vocal fold vibrations. The consequences are perturbations of the acoustic speech signal which are perceived as a hoarse voice. By means of appropriate image processing techniques, the vocal fold dynamics are extracted from digital high-speed videos. This study addresses the approach to obtain a parametric description of the spatio-temporal characteristics of the vocal fold oscillations for the aim of classification. For this purpose a biomechanical vocal fold model is introduced. An automatic optimization procedure is developed for fitting the model dynamics to the observed vocal fold oscillations. Thus, the resulting parameter values represent a specific vibration pattern and serve as an objective quantification measure. Performance and reliability of the optimization procedure are validated with synthetically generated data sets. The high-speed videos of two normal voice subjects and six patients suffering from different voice disorders are processed. The resulting model parameters represent a rough approximation of physiological parameters along the entire vocal folds.

Calibration of laryngeal endoscopic high-speed image sequences by an automated detection of parallel laser line projections.

High-speed laryngeal endoscopic systems record vocal fold vibrations during phonation in real-time. For a quantitative analysis of vocal fold dynamics a metrical scale is required to get absolute laryngeal dimensions of the recorded image sequence. For the clinical use there is no automated and stable calibration procedure up to now. A calibration method is presented that consists of a laser projection device and the corresponding image processing for the automated detection of the laser calibration marks. The laser projection device is clipped to the endoscope and projects two parallel laser lines with a known distance to each other as calibration information onto the vocal folds. Image processing methods automatically identify the pixels belonging to the projected laser lines in the image data. The line detection bases on a Radon transform approach and is a two-stage process, which successively uses temporal and spatial characteristics of the projected laser lines in the high-speed image sequence. The robustness and the applicability are demonstrated with clinical endoscopic image sequences. The combination of the laser projection device and the image processing enables the calibration of laryngeal endoscopic images within the vocal fold plane and thus provides quantitative metrical data of vocal fold dynamics.

Spatiotemporal classification of vocal fold dynamics by a multimass model comprising time-dependent parameters.

A model-based approach is proposed to objectively measure and classify vocal fold vibrations by left-right asymmetries along the anterior-posterior direction, especially in the case of nonstationary phonation. For this purpose, vocal fold dynamics are recorded in real time with a digital high-speed camera during phonation of sustained vowels as well as pitch raises. The dynamics of a multimass model with time-dependent parameters are matched to vocal fold vibrations extracted at dorsal, medial, and ventral positions by an automatic optimization procedure. The block-based optimization accounts for nonstationary vibrations and compares the vocal fold and model dynamics by wavelet coefficients. The optimization is verified with synthetically generated data sets and is applied to 40 clinical high-speed recordings comprising normal and pathological voice subjects. The resulting model parameters allow an intuitive visual assessment of vocal fold instabilities within an asymmetry diagram and are applicable to an objective quantification of asymmetries.

Registration of PE segment contour deformations in digital high-speed videos.

Oncologic therapy of laryngeal cancer may necessitate a total excision of the larynx which results in loss of voice. Voice rehabilitation can be achieved using mucosal tissue vibrations at the upper part of the esophagus which serves as substitute voice generating element (PE segment). The quality of the substitute voice is closely related to vibratory characteristics of the PE segment. By means of a high-speed camera the dynamics of the PE segment can be recorded in real-time. Using image processing the deformations of the PE segment are extracted from the image series as deforming contours. Commonly, the characterization of PE dynamics bases on the spectral analysis of the time varying contour area. However, this constitutes an integral approach which masks most of the specific dynamics of PE deformations. We present an algorithm that automatically registers one segmented contour in a frame of the video sequence to the contour in the next frame to derive discrete 2-D trajectories of PE vibrations. By concatenation of the obtained transformations this approach provides a total registration of PE segment contours. We suggest a mixed-integer programming formulation for the problem that combines an advanced outlier and deformation handling with the introduction of dummy points in regions that newly open up, and that includes normal information in the objective function to avoid unwanted deformations. Numerical experiments show that the implemented alternate convex search algorithm produces robust results which is demonstrated at the example of five high-speed recordings of laryngectomee subjects.

Model-based classification of nonstationary vocal fold vibrations.

Classification of vocal fold vibrations is an essential task of the objective assessment of voice disorders. For historical reasons, the conventional clinical examination of vocal fold vibrations is done during stationary, sustained phonation. However, the conclusions drawn from a stationary phonation are restricted to the observed steady-state vocal fold vibrations and cannot be generalized to voice mechanisms during running speech. This study addresses the approach of classifying real-time recordings of vocal fold oscillations during a nonstationary phonation paradigm in the form of a pitch raise. The classification is based on asymmetry measures derived from a time-dependent biomechanical two-mass model of the vocal folds which is adapted to observed vocal fold motion curves with an optimization procedure. After verification of the algorithm performance the method was applied to clinical problems. Recordings of ten subjects with normal voice and ten dysphonic subjects have been evaluated during stationary as well as nonstationary phonation. In the case of nonstationary phonation the model-based classification into "normal" and "dysphonic" succeeds in all cases, while it fails in the case of sustained phonation. The nonstationary vocal fold vibrations contain additional information about vocal fold irregularities, which are needed for an objective interpretation and classification of voice disorders.

Classification of unilateral vocal fold paralysis by endoscopic digital high-speed recordings and inversion of a biomechanical model.

Hoarseness in unilateral vocal fold paralysis is mainly due to irregular vocal fold vibrations caused by asymmetries within the larynx physiology. By means of a digital high-speed camera vocal fold oscillations can be observed in real-time. It is possible to extract the irregular vocal fold oscillations from the high-speed recordings using appropriate image processing techniques. An inversion procedure is developed which adjusts the parameters of a biomechanical model of the vocal folds to reproduce the irregular vocal fold oscillations. Within the inversion procedure a first parameter approximation is achieved through a knowledge-based algorithm. The final parameter optimization is performed using a genetic algorithm. The performance of the inversion procedure is evaluated using 430 synthetically generated data sets. The evaluation results comprise an error estimation of the inversion procedure and show the reliability of the algorithm. The inversion procedure is applied to 15 healthy voice subjects and 15 subjects suffering from unilateral vocal fold paralysis. The optimized parameter sets allow a classification of pathologic and healthy vocal fold oscillations. The classification may serve as a basis for therapy selection and quantification of therapy outcome in case of unilateral vocal fold paralysis.

Quantitative detection of substitute voice generator during phonation in patients undergoing laryngectomy.

OBJECTIVE: To evaluate the vibration pattern of the substitute voice generator of patients who have undergone laryngectomy. For automatic quantification of the oscillations of the pharyngoesophageal (PE) segments, image processing of digital high-speed video sequences is applied. DESIGN: Physiologic analysis. SETTING: An acute care hospital. PATIENTS: Endoscopic recordings were taken of 10 men who underwent laryngectomy (mean +/- SD age, 61.5 +/- 5.2 years) during sustained phonation of a vowel using a 90 degree endoscope coupled to a high-speed camera. MAIN OUTCOME MEASURES: An image-processing algorithm was developed to automatically define the pseudoglottis in each recording and track its movements. RESULTS: The clinical assessment of the high-speed technique for the endoscopic examination of the substitute voice generator yields the following results. The forms and oscillation characteristics of the pseudoglottides varied considerably: 3 pseudoglottides were circular, 6 were split shaped, and 1 was triangle shaped. A quasi-periodic opening and closing were observed and automatically detected by the described algorithm in each recording independently from quality of the recording and from morphologic and oscillation characteristics of the PE segment. The frequencies of the extracted oscillations of the pseudoglottides correspond to the structure of the acoustic signals. CONCLUSIONS: Automatic image processing of PE segments derived from high-speed endoscopic recordings enables the detection and quantification of the substitute voice generator's oscillations in high temporal resolution. These data directly prove that the detected pseudoglottis is the source of the substitute voice. Close relations between substitute voice and functional properties of the PE segment exist. In the future, these data will be interpreted by applying biomechanical models of the PE segment. Presumably, results may help to optimize surgical and adaptive procedures for specific substitute voice restoration.

Quantitative investigation of the vibration pattern of the substitute voice generator.

After a total excision of the larynx, mucosal tissue at the upper part of the esophagus can be used as a substitute voice generating element. The properties of the tissue dynamics are closely related to the substitute voice quality. The process of substitute voice is investigated by recording simultaneously the acoustic signal with a microphone and the vibrations of the voice generator with a digital high-speed camera. We propose an automatic image-processing technique which is applied to analyze the vibration pattern of the substitute voice generating element. First, an initialization step detects the voice generator within a high-speed sequence. Second, a combination of a threshold technique and an active contour algorithm tracks the tissue deformations of the substitute voice generator. The applicability of the algorithm is shown in three high-speed recordings. For the first time, tissue deformations of substitute voice generating elements are successfully tracked. The results of the image processing procedure are used to describe quantitatively the temporal properties of the substitute voice generator. Comparisons of the spectral components of tissue deformations and tracheoesophageal voice signals reveal the close relationship between the vibration pattern of the substitute voice generator and the quality of substitute voice.