Sara Moccia

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

BACKGROUND Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast). OBJECTIVE This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches. METHODS This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. DISCUSSION Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. CONCLUSION No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.

Confident texture-based laryngeal tissue classification for early stage diagnosis support

Abstract. Early stage diagnosis of laryngeal squamous cell carcinoma (SCC) is of primary importance for lowering patient mortality or after treatment morbidity. Despite the challenges in diagnosis reported in the clinical literature, few efforts have been invested in computer-assisted diagnosis. The objective of this paper is to investigate the use of texture-based machine-learning algorithms for early stage cancerous laryngeal tissue classification. To estimate the classification reliability, a measure of confidence is also exploited. From the endoscopic videos of 33 patients affected by SCC, a well-balanced dataset of 1320 patches, relative to four laryngeal tissue classes, was extracted. With the best performing feature, the achieved median classification recall was 93% [interquartile range (IQR)=6%]. When excluding low-confidence patches, the achieved median recall was increased to 98% (IQR=5%), proving the high reliability of the proposed approach. This research represents an important advancement in the state-of-the-art computer-assisted laryngeal diagnosis, and the results are a promising step toward a helpful endoscope-integrated processing system to support early stage diagnosis.

Physiological Parameter Estimation from Multispectral Images Unleashed

Multispectral imaging in laparoscopy can provide tissue reflectance measurements for each point in the image at multiple wavelengths of light. These reflectances encode information on important physiological parameters not visible to the naked eye. Fast decoding of the data during surgery, however, remains challenging. While model-based methods suffer from inaccurate base assumptions, a major bottleneck related to competing machine learning-based solutions is the lack of labelled training data. In this paper, we address this issue with the first transfer learning-based method to physiological parameter estimation from multispectral images. It relies on a highly generic tissue model that aims to capture the full range of optical tissue parameters that can potentially be observed in vivo. Adaptation of the model to a specific clinical application based on unlabelled in vivo data is achieved using a new concept of domain adaptation that explicitly addresses the high variance often introduced by conventional covariance-shift correction methods. According to comprehensive in silico and in vivo experiments our approach enables accurate parameter estimation for various tissue types without the need for incorporating specific prior knowledge on optical properties and could thus pave the way for many exciting applications in multispectral laparoscopy.

Learning-based classification of informative laryngoscopic frames

BACKGROUND AND OBJECTIVE Early-stage diagnosis of laryngeal cancer is of primary importance to reduce patient morbidity. Narrow-band imaging (NBI) endoscopy is commonly used for screening purposes, reducing the risks linked to a biopsy but at the cost of some drawbacks, such as large amount of data to review to make the diagnosis. The purpose of this paper is to present a strategy to perform automatic selection of informative endoscopic video frames, which can reduce the amount of data to process and potentially increase diagnosis performance. METHODS A new method to classify NBI endoscopic frames based on intensity, keypoint and image spatial content features is proposed. Support vector machines with the radial basis function and the one-versus-one scheme are used to classify frames as informative, blurred, with saliva or specular reflections, or underexposed. RESULTS When tested on a balanced set of 720 images from 18 different laryngoscopic videos, a classification recall of 91% was achieved for informative frames, significantly overcoming three state of the art methods (Wilcoxon rank-signed test, significance level = 0.05). CONCLUSIONS Due to the high performance in identifying informative frames, the approach is a valuable tool to perform informative frame selection, which can be potentially applied in different fields, such us computer-assisted diagnosis and endoscopic view expansion.

Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation

Stereo-ElectroEncephaloGraphy (SEEG) is a surgical procedure that allows brain exploration of patients affected by focal epilepsy by placing intra-cerebral multi-lead electrodes. The electrode trajectory planning is challenging and time consuming. Various constraints have to be taken into account simultaneously, such as absence of vessels at the electrode Entry Point (EP), where bleeding is more likely to occur. In this paper, we propose a novel framework to help clinicians in defining a safe trajectory and focus our attention on EP. For each electrode, a Maximum Intensity Projection (MIP) image was obtained from Computer Tomography Angiography (CTA) slices of the brain first centimeter measured along the electrode trajectory. A Gaussian Mixture Model (GMM), modified to include neighborhood prior through Markov Random Fields (GMM-MRF), is used to robustly segment vessels and deal with the noisy nature of MIP images. Results are compared with simple GMM and manual global Thresholding (Th) by computing sensitivity, specificity, accuracy and Dice similarity index against manual segmentation performed under the supervision of an expert surgeon. In this work we present a novel framework which can be easily integrated into manual and automatic planner to help surgeon during the planning phase. GMM-MRF qualitatively showed better performance over GMM in reproducing the connected nature of brain vessels also in presence of noise and image intensity drops typical of MIP images. With respect Th, it is a completely automatic method and it is not influenced by inter-subject variability.

Automatic workflow for narrow-band laryngeal video stitching

In narrow band (NB) laryngeal endoscopy, the clinician usually positions the endoscope near the tissue for a correct inspection of possible vascular pattern alterations, indicative of laryngeal malignancies. The video is usually reviewed many times to refine the diagnosis, resulting in loss of time since the salient frames of the video are mixed with blurred, noisy, and redundant frames caused by the endoscope movements. The aim of this work is to provide to the clinician a unique larynx panorama, obtained through an automatic frame selection strategy to discard non-informative frames. Anisotropic diffusion filtering was exploited to lower the noise level while encouraging the selection of meaningful image features, and a feature-based stitching approach was carried out to generate the panorama. The frame selection strategy, tested on on six pathological NB endoscopic videos, was compared with standard strategies, as uniform and random sampling, showing higher performance of the subsequent stitching procedure, both visually, in terms of vascular structure preservation, and numerically, through a blur estimation metric.

Computer-assisted liver graft steatosis assessment via learning-based texture analysis

PurposeFast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process.MethodsForty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was

Abstract: Physiological Parameter Estimation from Multispectral Images Unleashed

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