Patch Based Texture Classification of Thyroid Ultrasound Images using Convolutional Neural Network

Ultrasound (US) is an affordable and important imaging modality in medical imaging without potential hazards for patients and medical practitioners as compared to computed tomography which uses X-rays, magnetic resonance imaging which uses magnetic field and radio waves that could heat up the patient's body during long examinations, nuclear imaging, etc. Texture classification of anatomical structures in US images is an essential step for disease diagnosis and monitoring. In this work, we employed a convolutional neural network to segment thyroid gland in US images. This is particularly important for thyroid diseases diagnosis as they involve changes in the shape and size of the thyroid over time. The training of the Convolutional Neural Network (CNN) was not done directly on the acquired US images but on texture database that is created by dividing the thyroid US images of size 760 × 500 pixels into smaller texture patches of size 20 × 20 pixels. We obtained a Dice coefficient (DC) of 0.876 and Hausdorff Distance (HD) of 7.3 using the trained CNN that classifies the thyroid tissues as thyroid or non-thyroid. This approach was compared to the classic image processing approaches like active contours with edges (ACWE), graph cut (GC) and pixel-based classifier (PBC) which obtained a DC of 0.805, 0.745 and 0.666 respectively and Volumetric and Mass-Spring Models which obtained a HD of 11.1 and 9.8 respectively.

Cardiotocograph Data Classification Improvement by Using Empirical Mode Decomposition

This work proposes to study the fetal heart rate (FHR) signal based on information about its dynamics as a signal resulting from the modulation by the autonomic nervous system. The analysis is performed using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique. The main idea is to extract a set of signal features based on that technique and also conventional time-domain features proposed in the literature in order to study their performance by using a support vector machine (SVM) as a classifier. As a hypothesis, we postulate that by including CEEMDAN based features, the classification performance should improve compared with the performance achieved by conventional features. The proposed method has been evaluated using real FHR data extracted from the open access CTU-UHB database. Results show that the classification performance improved from 67, 6% using only conventional features, to 71, 7% by incorporating CEEMDAN based features.

A Preliminary Study on Automatic Characterization and Classification of Vascular Patterns of Contact Endoscopy Images

The structure and organization of blood vessels in the vocal fold go through changes during the advancement from healthy to benign and further on to malignant stages. Contact Endoscopy (CE) is an optical technique providing real-time information related to the vascular structure of laryngeal mucosa. However, this technique comes with subjectivity in the interpretation of vascular patterns. In this study, a novel automated approach is proposed for vessel pattern charac-terization and classification of larynx CE + Narrow Band Imaging (NBI) images. This method is mainly based on the computation of indicators related to the level of disorder of vessels. 12 features were extracted from the indicators and were fed into two supervised classifiers. Linear Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) showed an accuracy of 95.76% and 93.92% for vascular patterns and 86.04% and 82.23% for larynx histopathologies classification, respectively. These promising results show that the proposed method can potentially solve the subjectivity issues of CE.

Evaluation of Vascular Patterns Using Contact Endoscopy and Narrow-Band Imaging (CE-NBI) for the Diagnosis of Vocal Fold Malignancy

The endoscopic detection of perpendicular vascular changes (PVC) of the vocal folds has been associated with vocal fold cancer, dysplastic lesions, and papillomatosis, according to a classification proposed by the European Laryngological Society (ELS). The combination of contact endoscopy with narrow-band imaging (NBI-CE) allows intraoperatively a highly contrasted, real-time visualization of vascular changes of the vocal folds. Aim of the present study was to determine the association of PVC to specific histological diagnoses, the level of interobserver agreement in the detection of PVC, and their diagnostic effectiveness in diagnosing laryngeal malignancy. The evaluation of our data confirmed the association of PVC to vocal fold cancer, dysplastic lesions, and papillomatosis. The level of agreement between the observers in the identification of PVC was moderate for the less-experienced observers and almost perfect for the experienced observers. The identification of PVC during NBI-CE proved to be a valuable indicator for diagnosing malignant and premalignant lesions.

Design of an Auscultation System for Phonoangiography and Monitoring of Carotid Artery Diseases

Cerebrovascular diseases such as stenosis of the carotid artery are accountable for about 1 million death per year across Europe. Diagnostic tools like US, angiography or MRI require specific hardware and highly depend on the experience of the examining clinician. In contrast auscultation with a stethoscope can be used to screen for bruits - audible vascular sounds associated with turbulent blood flow. Dynamical changes in the flow due to pathological narrowing of the vessel can indicate the need for additional diagnostic investigations. A reliable auscultation setup is prerequisite to ensure high signal quality, adequate processing and the objective evaluation of a still subjectively assessed audible signal. We propose a computer assisted auscultation device for the characterisation of carotid bruits to facilitate the assessment of long-term changes in the vessel condition. Main goal of this work are design considerations regarding the mechanical interface of the proposed system to the skin. An experimental setup was used to compare the signal quality and morphology of different setups to a digital stethoscope. A combined system with two different interface configurations is proposed, current limitations of the system and potential improvements are discussed.

Novel Ultrasound Texture Based Similarity Metric Using Autoregressive Modelling

Designing an ultrasound (US) specific similarity metric is essential in integrating advanced techniques like image segmentation and registration to US based interventional procedures. Applying conventional similarity metrics to ultrasound images is hampered by intrinsic noise patterns in an US image. In this work, we propose a texture based similarity metric (TexSimAR) using Autoregressive (AR) modelling. The key idea is to treat an US image as data resulting from a dynamical process which can be parametrically modelled. Using this approach it is possible to compute a parametric spectrum of individual US images and subsequently use it to estimate a similarity value between them. For evaluation, we used thyroid US images and similarity values were calculated between thyroid and non-thyroid regions. A cost function was designed to compare TexSimAR with other conventional similarity metrics. TexSimAR clearly distinguished between thyroid and non-thyroid regions outperfoming the conventional similarity metrics.

Higher Order Statistical Analysis for Thyroid Texture Classification and Segmentation in 2D ultrasound Images

Ultrasound (US) imaging is one of the most cost-effective imaging modality that utilizes sound waves for generating medical images of anatomical structure. However, the presence of speckle noise and low contrast in the US images makes it difficult to use for proper classification of anatomical structures in clinical scenarios. Hence, it is important to devise a method that is robust and accurate even in the presence of speckle noise and is not affected by the low image contrast. In this work, a novel approach for thyroid texture characterization based on extracting features utilizing higher order spectral analysis (HOSA) was used. A Support Vector Machine (SVM) was applied on the extracted features to classify the thyroid texture. Since HOSA is a well suited technique for processing non-Gaussian data involving non-linear dynamics, good classification of thyroid texture can be obtained in US images as they also contain non-Gaussian Speckle noise and nonlinear characteristics. A final accuracy of 93.27%, sensitivity of 0.92 and specificity of 0.62 were obtained using the proposed approach.

Novel Similarity Metric for Image-Based Out-Of-Plane Motion Estimation in 3D Ultrasound

Over the past decade, Freehand 3D Ultrasound(US) reconstruction using only image information has become a widely researched topic because it eliminates the need for an external tracking system and provides real-time volumetric information. But most of the state-of-art methods are inhibited by their inability to find a simple and robust similarity metric that could learn and estimate the spatial transformation between two US slices in a US sweep. In this work, we propose a novel similarity metric (TexSimAR), which computes the similarity value between two consecutive US images by correlating the parametric representation of the image-texture instead of the image itself. The purpose of this approach is to capture and compare the dynamics in the texture characteristics of two US images. We modelled these dynamics using a parametrical auto-regressive (AR) model. Experiments were performed on forearm datasets of three subjects. For every pair of consecutive US slices, we computed our TexSimAR similarity value and out-of-plane transformation from the ground truth to train a Support Vector Machine (SVM) based regression model, which was then used to predict the out-of-plane transformation with the similarity value as input. The proposed method shows promising results with predictions better than state-of-the-art methods even with 1/8^th of training data compared to other methods in the literature.

Improved Acquisition of Vibroarthrographic Signals of the Knee Joint

This paper presents an improved solution for vibroarthrographic measurements. Four different setups for sensor attachment to the knee were assessed with a focus on the stability and reproducibility of the measured signals. By means of power spectral density estimates, the main signal components were compared and afterwards evaluated by conducting a cross-correlation analysis.

Assessment of Sound Features for Needle Perforation Event Detection

This paper studies the use of non-invasive acoustic emission recordings for clinical device tracking. In particular, audio signals recorded at the proximal end of a needle are used to detect perforation events that occur when the needle tip crosses internal tissue layers.A comparative study is performed to assess the capacity of different features and envelopes in detecting perforation events. The results obtained from the considered experimental setup show a statistically significant correlation between the extracted envelopes and the perforation events, thus leading the way for future development of perforation detection algorithms.

Computer Assisted Auscultation System for Phonoangiography of the Carotid Artery

Cerebrovascular diseases such as stenosis, atherosclerosis or distention of the carotid artery are accountable for about 1 million death per year across Europe. Diagnostic tools like ultrasound imaging, angiography or magnetic resonance-based imaging require specific hardware and highly depend on the experience of the examining clinician. In contrast auscultation with a stethoscope can be used to screen for carotid bruits - audible vascular sounds associated with turbulent blood flow - a method called phonoangiography. A reliable auscultation setup is prerequisite to ensure high signal quality, adequate processing and the objective evaluation of this audible signal. We propose a computer assisted auscultation system for the acquisition of vascular sounds of the carotid. The system comprises of an auscultation device, a smartphone-based control application and cloud-based signal analysis and storage. It is designed to facilitate the objective assessment, screening and monitoring of long-term changes in the vessel condition based on auscultation of the carotid artery.

Audio waves and its loss of energy in puncture needles

The location of a puncture needle’s tip and the resistance of tissue against puncture are crucial information for clinicians during a percutaneous procedure. The tip location and needle alignment can be observed by image guidance. Tactile information caused by tissue resistance to rupture, allow clinicians the perception of structural changes during puncture. Nevertheless, this sense is individual and subjective. To improve percutaneous procedures, the implementation of transducers to enhance or complement the senses offer objective feedback to the user. Known approaches are e.g. based on integrated force sensors. However, this is connected to higher device costs, sterilization and certification issues. A recent publication shows the implementation of an audio transducer clipped at the proximal end of the needle. This sensor is capable of acquiring emitted sounds of the distal tiptissue interaction that are transmitted over the needle structure. The interpretation of the measured audio signals is highly depended on the transmission over the needle, the tissue and, the penetration depth. To evaluate the influence of these parameters, this work implements a simplified experimental setup in a controlled environment with a minimum of noise and without micro tremors induced by clinician’s hands. A steel rod simulating a needle is inserted into pork meat of different thickness. A controlled impact covering the needle’s tip mimics tissue contact. The resulting signals are recorded and analyzed for better understanding of the system.

Foetal heart rate assessment by empirical mode decomposition and spectral analysis

This paper focuses on studying the time-variant dynamics involved in the foetal heart rate (FHR) response resulting from the autonomic nervous system modulation. It provides a comprehensive analysis of such dynamics by relating the spectral information involved in the FHR signal with foetal physiological characteristics. This approach is based on two signal processing methods: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and time-varying autoregressive (TV-AR) modelling. First, the CEEMDAN allows to decompose the signal into intrinsic mode functions (IMFs). Then, the TV-AR modelling allows to analyse their spectral dynamics. Results reveal that the IMFs can involve significant spectral information (p -value < 0.05) that can help to assess the foetal condition.

Texture differentiation using audio signal analysis with robotic interventional instruments

Robotic minimally invasive surgery (RMIS) has played an important role in the last decades. In traditional surgery, surgeons rely on palpation using their hands. However, during RMIS, surgeons use the visual-haptics technique to compensate the missing sense of touch. Various sensors have been widely used to retrieve this natural sense, but there are still issues like integration, costs, sterilization and the small sensing area that prevent such approaches from being applied. A new method based on acoustic emission has been recently proposed for acquiring audio information from tool-tissue interaction during minimally invasive procedures that provide user guidance feedback. In this work the concept was adapted for acquiring audio information from a RMIS grasper and a first proof of concept is presented. Interactions of the grasper with various artificial and biological texture samples were recorded and analyzed using advanced signal processing and a clear correlation between audio spectral components and the tested texture were identified.

Novel automated vessel pattern characterization of larynx contact endoscopic video images

Purpose

Contact endoscopy (CE) is a minimally invasive procedure providing real-time information about the cellular and vascular structure of the superficial layer of laryngeal mucosa. This method can be combined with optical enhancement methods such as narrow band imaging (NBI). However, these techniques have some problems like subjective interpretation of vascular patterns and difficulty in differentiation between benign and malignant lesions. We propose a novel automated approach for vessel pattern characterization of larynx CE + NBI images in order to solve these problems.

Methods

In this approach, five indicators were computed to characterize the level of vessel’s disorder based on evaluation of consistency of gradient and two-dimensional curvature analysis and then 24 features were extracted from these indicators. The method evaluated the ability of the extracted features to classify CE + NBI images based on the vascular pattern and based on the laryngeal lesions. Four datasets were generated from 32 patients involving 1485 images. The classification scenarios were implemented using four supervised classifiers.

Results

For classification of CE + NBI images based on the vascular pattern, polykernel support vector machine (SVM), SVM with radial basis function (RBF), k-nearest neighbor (kNN), and random forest (RF) show an accuracy of 97%, 96%, 96%, and 96%, respectively. For the classification based on the histopathology, Polykernel SVM showed an accuracy of 84%, 86% and 84%, RBF SVM showed an accuracy of 81%, 87% and 83%, kNN showed an accuracy of 89%, 87%, 91%, RF showed an accuracy of 90%, 88% and 91% for classification between benign histopathologies, between malignant histopathologies and between benign and malignant lesions, respectively.

Conclusion

These promising results show that the proposed method could solve the problem of subjectivity in interpretation of vascular patterns and also support the clinicians in the early detection of benign, pre-malignant and malignant lesions.

Parametrical modelling for texture characterization-A novel approach applied to ultrasound thyroid segmentation

Texture analysis is an important topic in Ultrasound (US) image analysis for structure segmentation and tissue classification. In this work a novel approach for US image texture feature extraction is presented. It is mainly based on parametrical modelling of a signal version of the US image in order to process it as data resulting from a dynamical process. Because of the predictive characteristics of such a model representation, good estimations of texture features can be obtained with less data than generally used methods require, allowing higher robustness to low Signal-to-Noise ratio and a more localized US image analysis. The usability of the proposed approach was demonstrated by extracting texture features for segmenting the thyroid in US images. The obtained results showed that features corresponding to energy ratios between different modelled texture frequency bands allowed to clearly distinguish between thyroid and non-thyroid texture. A simple k-means clustering algorithm has been used for separating US image patches as belonging to thyroid or not. Segmentation of thyroid was performed in two different datasets obtaining Dice coefficients over 85%.

Proximally placed signal acquisition sensoric for robotic tissue tool interactions

Robotic surgeries are still limited with respect to the surgeon’s natural senses. The tactile sense is exceptional important in conventional clinical procedures. To identify critical structures inside the tissue, palpation is a commonly used technique in conventional open surgeries. The underlying organ or pathological structures conditions (healthy, abnormally hard or soft) can for example be localized and assessed through this process. Palpation needs a tactile sense; however, that is commonly not available or limited in robotic surgeries. The palpation need was already addressed by several research groups that integrated complex sensor-feedback-systems into prototype surgical instruments for robotic systems. We propose a new technique to acquire data of the tissue tool interaction of the surgical instruments. The structure borne transmission path is used to measure acoustic emission (AE) at the outpatient (proximal) end of the instruments with the help of different sensors attached to the surface of the surgical tool. Initial tests were performed using a microphone in combination with a stethoscope. This setup showed promising results and a more integrated prototype was subsequently designed. A piezoelectric charge accelerometer was used as vibration sensor and compared to a MEMS microphone. A signal acquisition system was developed to acquire signals from both sensors in parallel. The sensors were then attached onto the shaft of a daVinci Prograsp Forceps instrument. According to the surgery observation, a series of simulated experiments was conducted. The tip of the grasper was swiped manually over a human subject’s dorsal and palmar hand side, lateral side of neck and over the carotid artery. Additionally, contact with soft tissue and other instruments were evaluated since these are events of interest during surgery. Advanced signal processing techniques allowed the identification and characterization of significant events such as palpation dynamics, contact and pulsation. Signals acquired by the MEMS microphone showed the most promising results. This approach will now be used to build a prototype for further evaluation in a clinical setup. The paper presents the first results that show that this novel technique can provide valuable information about the tool-tissue interaction in robotic surgery that typically can only be obtained through advanced distal sensor systems or actual human touch.

Vascular pattern detection and recognition in endoscopic imaging of the vocal folds

At present transoral laryngeal interventions are mainly observed and controlled by an external two dimensional direct microscopic view. This modality provides an overall view on the surgery situs in a straight line of sight. For treatment planning and appropriate documentation, an endoscopic inspection is mandatory prior to surgery. Nowadays a detailed endoscopic work-up of laryngeal lesions can be performed by contact endoscopy in combination with structure enhancement like Narrow Band Imaging. High resolution and magnification of up to 150 times provide detailed visualization of vascular structures and pathological changes of the tissue surface. In these procedures it is difficult however to localize the evaluated areas on large scale scenes like the microscopic view used for surgery. To provide a fast and easy image matching an automated vessel pattern recognition and allocation is presented. Endoscopic images depicting representative vessel structures of the vocal folds are selected out of contact endoscopy video scenes. These images are pre-processed for background homogenization. A Frangi Vessel Segmentation filter and morphological operations are used to extract the vessel structure and match it to the microscopic image. Using this method 4 detailed contact endoscopy images could be allocated in different scenes of the microscope video. This method can be used to simplify treatment planning and to prepare image data for documentation.

Foetal heart rate signal spectral analysis by using time-varying autoregressive modelling

During labour, foetal monitoring enables clinicians to prevent potential adverse outcomes, whose surveillance procedure is commonly based on analysis of cardiotocographic (CTG) signals. Unfortunately, this procedure is difficult because it involves human interpretation of highly complex signals. In order to improve the CTG assessment, different approaches based on signal processing techniques have been proposed. However, most of them do not consider the progression of the foetal response over time. In this work, we propose to study such progression along the foetal heart rate (FHR) signal by using spectral analysis based on time-varying autoregressive modelling. The main idea is to investigate if a particular FHR signal episode in the time-domain reflects dynamical changes in the frequency-domain that can help to assess the foetal condition. Results show that each FHR deceleration leaves a particular time-varying frequency signature described by the spectral energy components which could help to distinguish between a normal and a pathological foetus.

Anatomical Structure Segmentation in Ultrasound Volumes Using Cross Frame Belief Propagating Iterative Random Walks

Ultrasound (US) is widely used as a low-cost alternative to computed tomography or magnetic resonance and primarily for preliminary imaging. Since speckle intensity in US images is inherently stochastic, readers are often challenged in their ability to identify the pathological regions in a volume of a large number of images. This paper introduces a generalized approach for volumetric segmentation of structures in US images and volumes. We employ an iterative random walks (IRW) solver, a random forest learning model, and a gradient vector flow (GVF) based interframe belief propagation technique for achieving cross-frame volumetric segmentation. At the start, a weak estimate of the tissue structure is obtained using estimates of parameters of a statistical mechanics model of US tissue interaction. Ensemble learning of these parameters further using a random forest is used to initialize the segmentation pipeline. IRW is used for correcting the contour in various steps of the algorithm. Subsequently, a GVF-based interframe belief propagation is applied to adjacent frames based on the initialization of contour using information in the current frame to segment the complete volume by frame-wise processing. We have experimentally evaluated our approach using two different datasets. Intravascular ultrasound (IVUS) segmentation was evaluated using 10 pullbacks acquired at 20 MHz and thyroid US segmentation is evaluated on 16 volumes acquired at [Formula: see text] MHz. Our approach obtains a Jaccard score of [Formula: see text] for IVUS segmentation and [Formula: see text] for thyroid segmentation while processing each frame in [Formula: see text] for the IVUS and in [Formula: see text] for thyroid segmentation without the need of any computing accelerators such as GPUs.

Novel clinical device tracking and tissue event characterization using proximally placed audio signal acquisition and processing

We propose a new and complementary approach to image guidance for monitoring medical interventional devices (MID) with human tissue interaction and surgery augmentation by acquiring acoustic emission data from the proximal end of the MID outside the patient to extract dynamical characteristics of the interaction between the distal tip and the tissue touched or penetrated by the MID. We conducted phantom based experiments (n = 955) to show dynamic tool/tissue interaction during tissue needle passage (a) and vessel perforation caused by guide wire artery perforation (b). We use time-varying auto-regressive (TV-AR) modelling to characterize the dynamic changes and time-varying maximal energy pole (TV-MEP) to compute subsequent analysis of MID/tissue interaction characterization patterns. Qualitative and quantitative analysis showed that the TV-AR spectrum and the TV-MEP indicated the time instants of the needle path through different phantom objects (a) and clearly showed a perforation versus other generated artefacts (b). We demonstrated that audio signals acquired from the proximal part of an MID could provide valuable additional information to surgeons during minimally invasive procedures.

In-room ultrasound fusion combined with fully compatible 3D-printed holding arm - rethinking interventional MRI

There is no real need to discuss the potential advantages - mainly the excellent soft tissue contrast, nonionizing radiation, flow, and molecular information - of magnetic resonance imaging (MRI) as an intraoperative diagnosis and therapy system particularly for neurological applications and oncological therapies. Difficult patient access in conventional horizontal-field superconductive magnets, very high investment and operational expenses, and the need for special nonferromagnetic therapy tools have however prevented the widespread use of MRI as imaging and guidance tool for therapy purposes. The interventional use of MRI systems follows for the last 20+ years the strategy to use standard diagnostic systems and add more or less complicated and expensive components (eg, MRI-compatible robotic systems, specially shielded in-room monitors, dedicated tools and devices made from low-susceptibility materials, etc) to overcome the difficulties in the therapy process. We are proposing to rethink that approach using an in-room portable ultrasound (US) system that can be safely operated till 1 m away from the opening of a 3T imaging system. The live US images can be tracked using an optical inside-out approach adding a camera to the US probe in combination with optical reference markers to allow direct fusion with the MRI images inside the MRI suite. This leads to a comfortable US-guided intervention and excellent patient access directly on the MRI patient bed. This was combined with an entirely mechanical MRI-compatible 7 degrees of freedom holding arm concept, which shows that this test environment is a different way to create a cost-efficient and effective setup that combines the advantages of MRI and US by largely avoiding the drawbacks of current interventional MRI concepts.

Analysis of the foetal heart rate in cardiotocographic recordings through a progressive characterization of decelerations

The main purpose of this work is to propose a new method for characterization and visualization of FHR deceleration episodes in terms of their depth, length and location. This is performed through the estimation of a progressive baseline computed using a median filter allowing to identify and track the evolution of decelerations in cardiotocographic CTG recordings. The proposed method has been analysed using three representative cases of normal and pathological CTG recordings extracted from the CTU-UHB database freely available on the PhysioNet Website. Results show that both the progressive baseline and the parameterized deceleration episodes can describe different time-variant behaviour, whose characteristics and progression can help the observer to discriminate between normal and pathological FHR signal patterns. This opens perspectives for classification of non-reassuring CTG recordings as a sign of foetal acidemia.

Miniature CNT-based X-ray tube: assessment for use in intraoperative radiation therapy

Carbon nanotube (CNT) is a new technology used to generate gamma photons in X-ray tubes. CNTs, in comparison to other small X-ray sources, produce high X-ray intensities and as they are not based on a thermionic principle they considered cold electron sources with a very high conversion of electrical to photon energy. Their small size and other interesting properties could make them feasible for use in intraoperative radiation therapy applications. In this study, physical characteristics of the photon beam generated by the CNT-based X-ray source were assessed. A soft X-ray ionization chamber and a flat panel detector was used to measure dose and photon counts, respectively. The repetitively produced pulses had almost the same photon intensities with differences of less than 1% between them. For a typical selected pulse, the variation in the pulse amplitude was also insignificant, which shows a stable radiation exposure of the tube during the ON-mode. When moving from the center of the beam profile to the lateral distance of 25 mm, both intensity profile and dose profile showed a falling trend by a factor of almost 3 in the measured values.

We also tested the miniature tube with our novel radiation beam shaping collimator designed for a possible application to treat larynx tumor, which showed the possibility of interventional radiation therapy using this miniature source. An endoscopic camera attached to the system can also make it possible to optically visualize the radiation exposed area.

In conclusion, CNT-based X-ray source with suitable attached collimator to shape the beam of the source, seems to provide an opportunity to deliver radiation to a desired tumor area in minimally invasive image guided medical procedures mainly in the normal cavities of the body.