Microscope-integrated optical coherence tomography for in vivo human brain tumor detection with artificial intelligence.

OBJECTIVE: It has been shown that optical coherence tomography (OCT) can identify brain tumor tissue and potentially be used for intraoperative margin diagnostics. However, there is limited evidence on its use in human in vivo settings, particularly in terms of its applicability and accuracy of residual brain tumor detection (RTD). For this reason, a microscope-integrated OCT system was examined to determine in vivo feasibility of RTD after resection with automated scan analysis. METHODS: Healthy and diseased brain was 3D scanned at the resection edge in 18 brain tumor patients and investigated for its informative value in regard to intraoperative tissue classification. Biopsies were taken at these locations and labeled by a neuropathologist for further analysis as ground truth. Optical OCT properties were obtained, compared, and used for separation with machine learning. In addition, two artificial intelligence-assisted methods were utilized for scan classification, and all approaches were examined for RTD accuracy and compared to standard techniques. RESULTS: In vivo OCT tissue scanning was feasible and easily integrable into the surgical workflow. Measured backscattered light signal intensity, signal attenuation, and signal homogeneity were significantly distinctive in the comparison of scanned white matter to increasing levels of scanned tumor infiltration (p < 0.001) and achieved high values of accuracy (85%) for the detection of diseased brain in the tumor margin with support vector machine separation. A neuronal network approach achieved 82% accuracy and an autoencoder approach 85% accuracy in the detection of diseased brain in the tumor margin. Differentiating cortical gray matter from tumor tissue was not technically feasible in vivo. CONCLUSIONS: In vivo OCT scanning of the human brain has been shown to contain significant value for intraoperative RTD, supporting what has previously been discussed for ex vivo OCT brain tumor scanning, with the perspective of complementing current intraoperative methods for this purpose, especially when deciding to withdraw from further resection toward the end of the surgery.

The neurosurgical benefit of contactless in vivo optical coherence tomography regarding residual tumor detection: A clinical study.

Purpose: In brain tumor surgery, it is crucial to achieve complete tumor resection while conserving adjacent noncancerous brain tissue. Several groups have demonstrated that optical coherence tomography (OCT) has the potential of identifying tumorous brain tissue. However, there is little evidence on human in vivo application of this technology, especially regarding applicability and accuracy of residual tumor detection (RTD). In this study, we execute a systematic analysis of a microscope integrated OCT-system for this purpose. Experimental design: Multiple 3-dimensional in vivo OCT-scans were taken at protocol-defined sites at the resection edge in 21 brain tumor patients. The system was evaluated for its intraoperative applicability. Tissue biopsies were obtained at these locations, labeled by a neuropathologist and used as ground truth for further analysis. OCT-scans were visually assessed with a qualitative classifier, optical OCT-properties were obtained and two artificial intelligence (AI)-assisted methods were used for automated scan classification. All approaches were investigated for accuracy of RTD and compared to common techniques. Results: Visual OCT-scan classification correlated well with histopathological findings. Classification with measured OCT image-properties achieved a balanced accuracy of 85%. A neuronal network approach for scan feature recognition achieved 82% and an auto-encoder approach 85% balanced accuracy. Overall applicability showed need for improvement. Conclusion: Contactless in vivo OCT scanning has shown to achieve high values of accuracy for RTD, supporting what has well been described for ex vivo OCT brain tumor scanning, complementing current intraoperative techniques and even exceeding them in accuracy, while not yet in applicability.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction.

Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10-20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients' arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016-2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning.

Endocardial border detection is a key step in assessing left ventricular systolic function in echocardiography. However, this process is still not sufficiently accurate, and manual retracing is often required, causing time-consuming and intra-/inter-observer variability in clinical practice. To address these clinical issues, more accurate and normalized automatic endocardial border detection would be valuable. Here, we develop a deep learning-based method for automated endocardial border detection and left ventricular functional assessment in two-dimensional echocardiographic videos. First, segmentation of the left ventricular cavity was performed in the six representative projections for a cardiac cycle. We employed four segmentation methods: U-Net, UNet++, UNet3+, and Deep Residual U-Net. UNet++ and UNet3+ showed a sufficiently high performance in the mean value of intersection over union and Dice coefficient. The accuracy of the four segmentation methods was then evaluated by calculating the mean value for the estimation error of the echocardiographic indexes. UNet++ was superior to the other segmentation methods, with the acceptable mean estimation error of the left ventricular ejection fraction of 10.8%, global longitudinal strain of 8.5%, and global circumferential strain of 5.8%, respectively. Our method using UNet++ demonstrated the best performance. This method may potentially support examiners and improve the workflow in echocardiography.

Automatic Lumen Border Detection in IVUS Images Using Deep Learning Model and Handcrafted Features.

In the clinical analysis of Intravascular ultrasound (IVUS) images, the lumen size is an important indicator of coronary atherosclerosis, and is also the premise of coronary artery disease diagnosis and interventional treatment. In this study, a fully automatic method based on deep learning model and handcrafted features is presented for the detection of the lumen borders in IVUS images. First, 193 handcrafted features are extracted from the IVUS images. Then hybrid feature vectors are constructed by combining handcrafted features with 64 high-level features extracted from U-Net. In order to obtain the feature subsets with larger contribution, we employ the extended binary cuckoo search for feature selection. Finally, the selected 36-dimensional hybrid feature subset is used to classify the test images using dictionary learning based on kernel sparse coding. The proposed algorithm is tested on the publicly available dataset and evaluated using three indicators. Through ablation experiments, mean value of the experimental results (Jaccard: 0.88, Hausdorff distance: 0.36, Percentage of the area difference: 0.06) prove to be effective improving lumen border detection. Furthermore, compared with the recent methods used on the same dataset, the proposed method shows good performance and high accuracy.

Comparison of Four Different Techniques for Estimation of Left Ventricular Volumes Using Intraoperative Real Time Three Dimensional Transesophageal Echocardiography--A Prospective Observational Study.

OBJECTIVES: The primary objective of the present study was to compare cardiac output derived with four methods of QLab (Philips, Amsterdam, Netherlands) software using real-time three-dimensional (3D) transesophageal echocardiography, with cardiac output obtained with the 3D left ventricular outflow tract (LVOT) cardiac output method. The secondary objective was to assess left ventricular (LV) volumes, LV ejection fraction, and cardiac output derived with four different methods of real time 3D transesophageal echocardiography processed in QLab software and to determine whether these parameters differed among these four methods. DESIGN: A prospective observational study. SETTING: A tertiary referral center and a university level teaching hospital. PARTICIPANTS: The study comprised 50 patients scheduled for elective coronary artery bypass surgery without any concomitant valvular lesions. MEASUREMENTS AND MAIN RESULTS: Three-dimensional full-volume datasets were obtained in optimum conditions. The 3D datasets were analyzed using four different methods in QLab, version 9. In method A, LV volumes were derived without endocardial border adjustment. In method B, LV volumes were obtained after endocardial border adjustment in the long-axis view alone. In method C, the iSlice tool (Philips) was used to adjust the endocardial borders in 16 short-axis slices. In method D, endocardial borders were adjusted after dataset processing to obtain LV volumes. The cardiac output derived with the 3D echocardiography LVOT method was 3.93 ± 1.44 L/min, with method A was 3.26 ± 1.42 L/min, with method B was 3.51 ± 1.2 L/min, with method C was 4.01 ± 1.40 L/min, and with method D was 4.18 ± 1.58 L/min. There was a significant positive correlation between the cardiac output derived using the 3D LVOT method and method C (r = 0.71). CONCLUSIONS: Readjusting the endocardial border contours resulted in higher LV volumes than the volumes estimated using semiautomated border algorithms. The iSlice method produced the highest and the most accurate LV volumes, although it required the longest time to analyze and derive results. The ejection fraction obtained with all four methods of QLab demonstrated no statistical differences and had a strong correlation with the two-dimensional echocardiography-derived left ventricular ejection fraction.

IVUS images segmentation using spatial fuzzy clustering and hierarchical level set evolution.

The detection of the lumen and media-adventitia (MA) borders in intravascular ultrasound (IVUS) images is crucial for quantifying plaque burdens. The challenge of the segmentation work mainly roots in various artifacts in the image. Most of the published methods involve the establishment of complex models but do not behave well on images with artifacts. In this study, aiming at automatically delineating borders in IVUS frames acquired by 20 MHz ultrasound probes, we present a fuzzy clustering-initialized hierarchical level set evolution (FC-HLSE) method. A cluster selection strategy based on the spatial fuzzy c-means (FCM) is proposed to generate the initial value and regularization term of the level set evolution (LSE). The contour convergence splits into two LSE steps between which an ingenious contour extraction (consisting of the morphological processing, the seek and linear interpolation, the gradient-based and circular fitting-based refinement) is carried out. We evaluate the proposed methodology on the publicly available 435 images by comparing auto-segmented results with the ground truth. The performance of the method is quantified using the Jaccard measure (JM), the Hausdorff distance (HD), the percentage of area difference (PAD), the linear regression and Bland-Altman analysis. Results reveal that our method can handle images with or without artifacts. The algorithm is able to extract the lumen/MA border with the JM of 0.90/0.89, the HD of 0.31/0.40 mm, the PAD of 0.07/0.08 in average, which is better in some cases compared with several state-of-the-art methods.

Accurate assessment of LV function using the first automated 2D-border detection algorithm for small animals - evaluation and application to models of LV dysfunction.

Echocardiography is the most commonly applied technique for non-invasive assessment of cardiac function in small animals. Manual tracing of endocardial borders is time consuming and varies with operator experience. Therefore, we aimed to evaluate a novel automated two-dimensional software algorithm (Auto2DE) for small animals and compare it to the standard use of manual 2D-echocardiographic assessment (2DE). We hypothesized that novel Auto2DE will provide rapid and robust data sets, which are in agreement with manually assessed data of animals.2DE and Auto2DE were carried out using a high-resolution imaging-system for small animals. First, validation cohorts of mouse and rat cine loops were used to compare Auto2DE against 2DE. These data were stratified for image quality by a blinded expert in small animal imaging. Second, we evaluated 2DE and Auto2DE in four mouse models and four rat models with different cardiac pathologies.Automated assessment of LV function by 2DE was faster than conventional 2DE analysis and independent of operator experience levels. The accuracy of Auto2DE-assessed data in healthy mice was dependent on cine loop quality, with excellent agreement between Auto2DE and 2DE in cine loops with adequate quality. Auto2DE allowed for valid detection of impaired cardiac function in animal models with pronounced cardiac phenotypes, but yielded poor performance in diabetic animal models independent of image quality.Auto2DE represents a novel automated analysis tool for rapid assessment of LV function, which is suitable for data acquisition in studies with good and very good echocardiographic image quality, but presents systematic problems in specific pathologies.

Local edge-enhanced active contour for accurate skin lesion border detection.

BACKGROUND: Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. RESULT: Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. CONCLUSION: We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

A new nonparametric statistical approach to detect lumen and Media-Adventitia borders in intravascular ultrasound frames.

Intravascular ultrasound (IVUS) imaging is widely known as a powerful interventional imaging modality for diagnosing atherosclerosis, and for treatment planning. In this regard, the detection of lumen and media-adventitia (MA) borders is considered to be a vital process. However, the manual detection of these two borders by the physician is cumbersome due to the large number of frames in a sequence. In addition, no approved universal automatic method has been presented so far due to the great diversity in the appearance of the coronary artery in the images acquired by different IVUS systems. To this end, the present study aimed to provide a new border search theory on the radial profile, based upon the nonparametric statistical approach, and to develop a generic and fully automatic three-step process for extracting the lumen and MA borders in IVUS frames based on the proposed theory. Thereafter, the proposed theory and three-step process were evaluated on synthetic images, as well as on a test set of standard publicly available images, respectively. The results showed that our three-step process could segment the borders with ≥0.82 and with ≥0.75 Jaccard measure (JM) to manual borders in IVUS frames acquired by the 20 MHz and 40 MHz probes, respectively. Based on the results, the lumen and MA borders can be extracted automatically, and the border extraction process can be implemented in parallel for a polar image due to the capability of the present proposed method to estimate the borders for each angle independently.

Fractional-order Darwinian PSO-based feature selection for media-adventitia border detection in intravascular ultrasound images.

Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique. The feature selection technique is based on Fractional-order Darwinian particle swarm optimization (FODPSO) algorithm. By employing feature selection, 293-dimension features including multi-scale features, gray-scale features and morphological feature are reducing to 37-dimension. The border detection method with feature selection is tested on a public dataset extracted from in-vivo pullbacks of human coronary arteries, which contains 77 IVUS images. Three indicators, Jaccard (JACC), Hausdorff Distance (HD) and Percentage of Area Difference (PAD), are measured for quantitative evaluation. Detection with 293-dimension features obtains JACC 0.79, HD 1.41 and PAD 0.16, while detection with 37-dimension features obtains JACC 0.83, HD 1.27 and PAD 0.12, indicating that the FODPSO-based feature selection method improves MA border detection by JACC 0.04, HD 0.14 and PAD 0.04. Furthermore, the proposed border detection method acquires better performances compared with two other automatic methods conducted on the same dataset available in literature.

Detecting the Media-adventitia Border in Intravascular Ultrasound Images through a Classification-based Approach.

The detection of the media-adventitia (MA) border in intravascular ultrasound (IVUS) images is essential for vessel assessment and disease diagnosis. However, it remains a challenging task, considering the existence of plaque, calcification, and various artifacts. In this article, an effective method based on classification is proposed to extract the MA border in IVUS images. First, a novel morphologic feature describing the relative position of each structure relative to the MA border, called RPES for short, is proposed. Then, the RPES feature and other features are employed in a multiclass extreme learning machine (ELM) to classify IVUS images into nine classes including the MA border and other structures. At last, a modified snake model is employed to effectively detect the MA border in the rectangular domain, in which a modified external force field is constructed on the basis of local border appearances and classification results. The proposed method is evaluated on a public dataset with 77 IVUS images by three indicators in eight situations, such as calcification and a guide wire artifact. With the proposed RPES feature, detection performances are improved by more than 39 percent, which shows an apparent advantage in comparative experiments. Furthermore, compared with two other existing methods used on the same dataset, the proposed method achieves 18 of the best indicators among 24, demonstrating its higher capability in detecting the MA border.

Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding.

BACKGROUND AND OBJECTIVE: To ensure proper functioning of a Computer Aided Diagnosis (CAD) system for melanoma detection in dermoscopy images, it is important to accurately detect the border of the lesion. This paper proposes a method developed by the authors to address this problem. METHODS: The algorithm for segmentation of skin lesions in dermoscopy images is based on fuzzy classification of pixels and subsequent histogram thresholding. RESULTS: This method participated in the 2016 and 2017 ISBI (International Symposium on Biomedical Imaging) Challenges, hosted by the ISIC (International Skin Imaging Collaboration). It was tested against two public databases containing 379 and 600 images respectively, and compared using the same defined metrics (Accuracy, Dice Coefficient, Jaccard Index, Sensitivity and Specificity) with the rest of participating state-of-the-art work, obtaining good results: (0.934, 0.869, 0.791, 0.870 and 0.978) and (0.884, 0.760, 0.665, 0.869 and 0.923) respectively, ranking 9th and 15th out of a total of 21 and 28 participants respectively using the Jaccard Index (which was the indicator used as a basis for ranking) and the 1st in the 2017 Challenge using the Sensitivity. CONCLUSION: The method has been proven to be robust and reliable. It's main contribution is the very design of the algorithm, highly innovative, which could also be used to deal with other segmentation problems of a similar nature.

Granular Cell Tumor Imaging Using Optical Coherence Tomography.

BACKGROUND: Granular cell tumor (GCT) is a relatively uncommon tumor that may affect the skin. The tumor can develop anywhere on the body, although it is predominately seen in oral cavities and in the head and neck regions. Here, we present the results of optical coherence tomography (OCT) imaging of a large GCT located on the abdomen of a patient. We also present an analytical method to differentiate between healthy tissue and GCT tissues. MATERIALS AND METHODS: A multibeam, Fourier domain, swept source OCT was used for imaging. The OCT had a central wavelength of 1305 ± 15 nm and lateral and axial resolutions of 7.5 and 10 µm, respectively. Qualitative and quantitative analyses of the tumor and healthy skin are reported. RESULTS: Abrupt changes in architectures of the dermal and epidermal layers in the GCT lesion were observed. These architectural changes were not observed in healthy skin. DISCUSSION: To quantitatively differentiate healthy skin from tumor regions, an optical attenuation coefficient analysis based on single-scattering formulation was performed. The methodology introduced here could have the capability to delineate boundaries of a tumor prior to surgical excision.

Lumen and media-adventitia border detection in IVUS images using texture enhanced deformable model.

Lumen and media-adventitia (MA) borders in intravascular ultrasound (IVUS) images are critical for assessing the dimensions of vascular structures and providing plaque information in the diagnosis and navigation of vascular interventions. However, manual delineation of the lumen and MA borders is an intricate and time-consuming process. In this paper, a texture-enhanced deformable model (TEDM) is proposed to accurately detect these borders by incorporating texture information with the morphological factors of deformable model. An ensemble support vector machine classifier is used to classify IVUS pixels presented by texture features into different tissue types. The image regionalization maps of different tissue types are further used for texture enhancement modules in the TEDM. The proposed TEDM method has been tested on 1500 images from 15 clinical IVUS datasets by comparing with the manual delineations. Evaluation results demonstrate that our method can accurately detect lumen and MA surfaces with small surface distance errors of 0.17 and 0.19 mm, respectively. Accurate segmentation results provide 2D measurements of MA/lumen areas and 3D vessel visualizations for vascular interventions.

Automatic methods for alveolar bone loss degree measurement in periodontitis periapical radiographs.

BACKGROUND AND OBJECTIVE: Periodontitis involves progressive loss of alveolar bone around the teeth. Hence, automatic alveolar bone loss measurement in periapical radiographs can assist dentists in diagnosing such disease. In this paper, we propose an automatic length-based alveolar bone loss measurement system with emphasis on a cementoenamel junction (CEJ) localization method: CEJ_LG. METHOD: The bone loss measurement system first adopts the methods TSLS and ABLifBm, which we presented previously, to extract teeth contours and bone loss areas from periodontitis radiograph images. It then applies the proposed methods to locate the positions of CEJ, alveolar crest (ALC), and apex of tooth root (APEX), respectively. Finally the system computes the ratio of the distance between the positions of CEJ and ALC to the distance between the positions of CEJ and APEX as the degree of bone loss for that tooth. The method CEJ_LG first obtains the gradient of the tooth image then detects the border between the lower enamel and dentin (EDB) from the gradient image. Finally, the method identifies a point on the tooth contour that is horizontally closest to the EDB. RESULTS: Experimental results on 18 tooth images segmented from 12 periodontitis periapical radiographs, including 8 views of upper-jaw teeth and 10 views of lower-jaw teeth, show that 53% of the localized CEJs are within 3 pixels deviation (∼ 0.15 mm) from the positions marked by dentists and 90% have deviation less than 9 pixels (∼ 0.44 mm). For degree of alveolar bone loss, more than half of the measurements using our system have deviation less than 10% from the ground truth, and all measurements using our system are within 25% deviation from the ground truth. CONCLUSION: Our results suggest that the proposed automatic system can effectively estimate degree of horizontal alveolar bone loss in periodontitis radiograph images. We believe that our proposed system, if implemented in routine clinical practice, can serve as a valuable tool for early and accurate diagnosis of alveolar bone loss in periodontal diseases and also for assessing the status of alveolar bone following various types of non surgical and surgical and regenerative therapy. For overall system improvement, a more objective comparison by using transgingival bone measurement with a periodontal probe as the ground truth and enhancing the localization algorithms of these three critical points are the two major tasks.

Segmentation of skin lesion using Cohen-Daubechies-Feauveau biorthogonal wavelet.

This paper presents a novel technique for segmentation of skin lesion in dermoscopic images based on wavelet transform along with morphological operations. The acquired dermoscopic images may include artifacts inform of gel, dense hairs and water bubble which make accurate segmentation more challenging. We have also embodied an efficient approach for artifacts removal and hair inpainting, to enhance the overall segmentation results. In proposed research, color space is also analyzed and selection of blue channel for lesion segmentation have confirmed better performance than techniques which utilizes gray scale conversion. We tackle the problem by finding the most suitable mother wavelet for skin lesion segmentation. The performance achieved with 'bior6.8' Cohen-Daubechies-Feauveau biorthogonal wavelet is found to be superior as compared to other wavelet family. The proposed methodology achieves 93.87 % accuracy on dermoscopic images of PH2 dataset acquired at Dermatology Service of Hospital Pedro Hispano, Matosinhos, Portugal.

Shot boundary detection in endoscopic surgery videos using a variational Bayesian framework.

PURPOSE: Over the last decade, the demand for content management of video recordings of surgical procedures has greatly increased. Although a few research methods have been published toward this direction, the related literature is still in its infancy. In this paper, we address the problem of shot detection in endoscopic surgery videos, a fundamental step in content-based video analysis. METHODS: The video is first decomposed into short clips that are processed sequentially. After feature extraction, we employ spatiotemporal Gaussian mixture models (GMM) for each clip and apply a variational Bayesian (VB) algorithm to approximate the posterior distribution of the model parameters. The proper number of components is handled automatically by the VBGMM algorithm. The estimated components are matched along the video sequence via their Kullback-Leibler divergence. Shot borders are defined when component tracking fails, signifying a different visual appearance of the surgical scene. RESULTS: Experimental evaluation was performed on laparoscopic videos containing a variable number of shots. Performance was measured via precision, recall, coverage and overflow metrics. The proposed method was compared with GMM and a shot detection method based on spatiotemporal motion differences (MotionDiff). The results demonstrate that VBGMM has higher performance than all other methods for most assessment metrics: precision and recall >80 %, coverage: 84 %. Overflow for VBGMM was worse than MotionDiff (37 vs. 27 %). CONCLUSIONS: The proposed method generated promising results for shot border detection. Spatiotemporal modeling via VBGMMs provides a means to explore additional applications such as component tracking.

Objective selection of short-axis slices for automated quantification of left ventricular size and function by cardiovascular magnetic resonance.

BACKGROUND: Quantification of left ventricular (LV) volume from cardiovascular magnetic resonance images relies on subjective and often challenging selection of short-axis (SAX) slices. We hypothesized that this could be solved by defining mitral annular (MA) plane and apex in long-axis (LAX) views, which could be combined with automated LV volume analysis that does not rely on manual tracing of the endocardial border. METHODS: SAX images from 50 subjects were analyzed using custom software. LV apex and insertion points of the mitral leaflets were marked on LAX views and used to approximate MA plane. End-systolic and end-diastolic LV volumes (ESV, EDV) were measured while including only slices or their parts located between MA plane and LV apex. Endocardial borders were automatically detected using our previously validated algorithm and also manually traced to obtain reference values. RESULTS: Selection of anatomic landmarks in LAX views allowed automated measurement of LV volumes without the need for subjective slice selection. Intertechnique comparisons resulted in high correlations (EDV: r=0.95; ESV: r=0.96) and small biases (1 and 9ml). Combined three-dimensional displays of LAX and SAX views with the MA plane showed that in 7/10 worst cases, intertechnique discordance was due to incorrect manual tracing at LV base that erroneously included part of atrial cavity in LV volume or excluded part of LV cavity, i.e., incorrect reference values. CONCLUSION: Defining the MA plane and apex in the LAX views obviates the need for subjective slice selection and eliminates errors in LV volume measurements.

Skin lesion image segmentation using Delaunay Triangulation for melanoma detection.

Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection.