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Coronavirus 2019 (SARS-CoV-2 or COVID-19) is an infectious disease affecting the respiratory system, which has caused a global pandemic and widespread morbidity and mortality in humans. It has mild to severe symptoms similar to those of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), resulting in multiorgan damage.1 Currently, two tests are available for detection of COVID-19 in the affected patients: diagnostic tests (current infection) and antibody tests (past infection). Diagnostic tests, such as reverse transcription polymerase chain reaction (RT-PCR) and antigen tests, are used for rapid diagnosis of COVID-19. Since false positives (FPs) are more common in antigen tests, RT-PCR is used as the gold standard for diagnosis of the disease. RT-PCR tests require an intensive lab work to acquire the results,2 and the cost of the test is a major concern in many countries that have a private health system. Although the PCR and antigen test can now provide a rapid diagnosis, the assessment of the lungs using medical imaging will provide information on disease burden. Also, faster and earlier detection of COVID-19 would help in isolating the affected patients sooner to alleviate the disease spread. Chest radiography (CXR) and computed tomography (CT) images are the conventional medical imaging modalities used in lung disease diagnosis.3,4 Though CT images are extensively used in the COVID-19 diagnosis,5–7 cost8 and radiation exposure are major concerns. CXR are preferred over CT images as they have less exposure to radiation and extensively available.9 Hence, in this study, CXR images are used for automatic diagnosis of COVID-19. Deep learning techniques are widely used in various fields, such as computer vision, machine vision, and speech recognition, among which computer vision is one of the most popular fields in which promising results to have been obtained in image classification tasks.10–12 In medical image analysis, deep learning has been widely investigated for computer-aided diagnosis and treatment.13,14 Several state-of-art methods have been proposed for the diagnosis of COVID-19 using CXR images15–19 and CT images5,20,21 based on deep learning techniques. The transfer learning approaches based on deep learning have been preferred in detection of COVID-19 due to the limited available dataset. Several studies have been implemented for COVID-19 diagnosis using the pretrained model as a feature extractor by implementing transfer learning techniques.22–24 Table 1 summarizes recent studies describing methods developed for detecting COVID-19 from CXR images; the overall datasets used for training and testing the model, accuracy, and the sensitivity are provided. Ozturk et al.18 developed the DarkCovidNet model for the detection of COVID-19 using multiclass classification. The model was developed using end-to-end architecture without adding any feature extraction techniques. Sensitivity of 85.35% was achieved by the proposed model. Wang and Wong25 proposed COVID-Net, a deep convolutional neural network (DCNN) developed by adopting machine-driven design exploration strategy. A multiclass classification was performed using CXR images in identifying COVID-19 from normal and non-COVID disease (i.e., pneumonia). Both qualitative and quantitative analysis were performed to determine the success of the model. The developed model was able to achieve a sensitivity of 91% for detecting COVID-19. Panwar et al.26 introduced nCOVnet, an algorithm developed based on a transfer learning model for faster diagnosis of COVID-19 from CXR images. The proposed model is built using VGG 16 as the base model. The model was able to achieve 97.62% in detecting COVID-19. Sethy et al.23 described a comparative study on Xception, ResNet18, ResNet50, Resnet 101, Inceptionv3, Inceptionresnetv2, GoogleNet, Densenet201, VGG16, VGG19, and AlexNet using the deep features extracted from the CNN layers and fed to the SVM classifier for classification. ResNet50 performed better compared with other classification models on a binary classification between viral pneumonia and COVID-19 with a classification accuracy of 95.38%. Jain et al.27 utilized ResNet50 model that was used in stage-I network model for distinguishing viral (including COVID-19) from bacterial pneumonia and normal cases using CXR images. Based on the result obtained, ResNet101 was used in stage-II network model to classify COVID-19 from other viral pneumonia. The model achieved an accuracy of 97% in COVID-19 detection. Narin et al.28 proposed three CNNs for the detection of COVID-19 through CXR images. The pretrained models chosen for comparative study were ResNet50, InceptionV3, and Inception-ResNetV2. A binary classification was performed between normal and COVID-19. ResNet50 model outperformed all the other models with the highest classification accuracy of 98%. Ioannis et al.22 compared VGG 19, Mobile Net, Inception, Xception, and Inception ResNetv2 pretrained models for the automatic detection of COVID-19 using CXR images. They implemented multiclass classification among COVID-19, normal, and pneumonia. MobileNetV2 achieved highest sensitivity rate of 98.6%. Asnaoui et al.29 put forward a comparative study on the pretrained DCNN models, namely VGG16, VGG19, Inception-ResNetV2, InceptionV3, ResNet50, DenseNet201, and MobileNetV2 for the classification of CXR into normal, bacteria, and coronavirus (multiclass classification). Inception-ResNetV2 model provided a sensitivity rate of 92.11% in detecting coronavirus. Hemdan et al.30 proposed COVIDX-Net a deep learning framework based on seven DCNNs namely, VGG19, Xception, ResNetV2, InceptionV3, Inception-ResNetV2, DenseNet201, and MobileNetV2 for the diagnosis of COVID-19 using x-ray images. The VGG19 and DenseNet201 achieved an accuracy of 90% compared with other models with F1-score of 0.89 for normal and 0.91 for COVID-19. Table 1 Methods described for diagnosis of COVID-19 from CXR and CT images.

Significance: Optical scattering signals obtained from tissue constituents contain a wealth of structural information. Conventional intensity features, however, are mostly dictated by the overall morphology and mean refractive index of these constituents, making it very difficult to exclusively sense internal refractive index fluctuations. Aim: We perform a systematic analysis to elucidate how changes in internal refractive index profile of cell nuclei can best be detected via optical scattering. Approach: We construct stochastically inhomogeneous nuclear models and numerically simulate their azimuth-resolved scattering patterns. We then process these two-dimensional patterns with the goal of identifying features that directly point to subnuclear structure. Results: Azimuth-dependent intensity variations over the side scattering range provide significant insights into subnuclear refractive index profile. A particular feature we refer to as contrast ratio is observed to be highly sensitive to the length scale and extent of refractive index fluctuations; further, this feature is not susceptible to changes in the overall size and mean refractive index of nuclei, thereby allowing for selective tracking of subnuclear structure that can be linked to chromatin distribution. Conclusions: Our analysis will potentially pave the way for scattering-based assessment of chromatin reorganization that is considered to be a key hallmark of precancer progression.

Oral cancer is one of the most common cancers worldwide, with high mortality rates. According to the International Agency for Research on Cancer, there were an estimated 377,000 new cases of lip and oral cavity cancers in 2020, with nearly 177,000 deaths worldwide.1 Despite advances in oncology therapy, mortality rates for oral cancer remain high over the past few decades. A majority of oral cancer patients do not have access to timely, quality diagnosis, and treatment, especially in rural areas, resulting in poor survival rates. The overall 5-year survival rate of diagnosed oral cancer patients is around 50% and has varied by race and area,2 whereas survival rates as high as 65% have been reported in developed countries; in some rural areas, they can be as low as 15% depending on the affected part of the oral cavity. The 2020 Cancer Statistics Report from India estimated that 66.6% of patients suffering from head and neck cancer were diagnosed at the locally advanced stage.3 In short, patient survival rates and prognosis are severely compromised when oral cancer patients are diagnosed at more advanced stages4,5 so that enhancing early diagnosis could mean a significant rise in positive survival outcomes.6,7 As oral squamous cell carcinoma (OSCC) accounts for ∼90% of all oral cancer,8,9 these two terms tend to be used interchangeably. OSCC, which originates as an epithelial dysplasia (from a histopathologic perspective), generally develops from precursor lesions termed as oral potentially malignant disorders (OPMDs).10,11 Nevertheless, it is not inevitable that all OPMDs, even the most commonly encountered such as oral lichen planus, leukoplakia, and erythroplakia,11 result in the subsequent development of malignancies.10,12 Diagnosing OPMDs as definable diseases is also challenging due to the numerous varieties, various forms, and overlapping features. However, studies10,13,14 have found that when an OPMD changes to a nonhomogeneous presentation, it is more likely to be considered as an adverse progression, in other words, nonhomogeneous lesions have a greater risk of malignant transformation as against homogeneous lesions. Hence, compared with defined diagnosis, distinguishing the potential malignant characteristics of OPMD is a greater concern. In summary, an ideal clinical prediction method should be employed to diagnose OSCC early and in particular to assess malignancy at the OPMD stage. Currently, conventional oral examination (COE) consisting of visual and tactile assessment (followed by tissue biopsy if there are any suspicious findings) is the most routine procedure in the management of oral cancer and precursor disease. However, one limitation for COE is that several features of oral cancer may appear benign and even mimic aphthous ulcers, and they are too clinically heterogeneous and subtle for general dentists to distinguish. Second, because of its invasive nature and sampling bias that can lead to underdiagnosis or misdiagnosis, biopsy is often not ideal as a screening tool.15 Furthermore, although specialists can recognize most of the characteristics that differentiate benign and cancerous lesions, the number of specialists and health resources are limited and concentrated across regions, causing a large part of the oral cancer burden to fall on low-resource communities. Therefore, the idea of establishing a cost-effective screening strategy as an adjunctive aid to the current procedures is gaining widespread popularity.16 Recently, deep learning techniques have exhibited a comparative advantage over feature-based methods in medical image analysis. A variety of studies17,18 showed that deep learning algorithms are able to surpass the performance of human experts in many disease recognition scenarios. In oral cancer diagnosis, deep learning methods also showed promising results for automatic analysis of pathology, confocal laser endomicroscopy (CLE) images, and fluorescence images. For example, Kumar et al.19 proposed a two-stage method that used a segmentation network and a random forest tree classifier to identify different stages of oral cancer in histological images. Aubreville et al.20 tested the deep convolutional neural network for OSCC diagnosis on CLE images, and the results showed that it outperformed the feature-based classification methods. Song et al.21,22 developed mobile connected devices to acquire fluorescence oral images and used them to identify oral disease. However, these methods all require expensive devices or a specifically designed screening platform, which are not accessible to everyone. In other words, patients are still required to go a professional clinic to receive disease diagnosis. With the rapid development of both imaging and sensing technologies in camera systems, the ubiquity of smartphones is equipped with higher quality, low-noise, and faster camera modules. Smartphone-based white light inspection methods23–25 are good solutions for acquiring oral images. Camalan et al.23 used a CNN-based network for classifying white light images as normal or suspicious; however, the patient sample is very limited: with only 54 cases. To build a reliable system, Welikala et al.24 collected more clinically labeled data for network training and evaluation, achieving a sensitivity of 52.13% and a specificity of 49.11% on multiclass classification task. However, we found that the clinical imaging capturing from hand-held smartphone cameras may exhibit a large variability, leading to poor diagnostic performance of the detection algorithm. For example, from a computer vision point of view, the shape and size of imaging lesions depend on the fields-of-view and focal distance, respectively. Unfortunately, a limited amount of research focuses on this problem. To address these challenges, we aim to explore reliable and robust smartphone-based white light image approaches, including an image-capturing method, resampling method, and high performance of CNN model, for oral disease recognition. Our main contributions are summarized as follows. 1. We propose a simple yet effective image-capturing method for consistent lesion position and focal distance over different images. The method allows direct focus on discriminative parts for disease recognition, without utilizing any region proposal methods,24,25 or relying on bounding box annotation24 by oral specialists. 2. We present a resampling method to alleviate the effect of image variability that introduced by the hand-held smartphone camera; at the same time, it can remedy the class imbalance problem. 3. We use one of the latest proposed convolutional neural networks (HRNet) for oral disease classification and achieve better results than common classification models. Analysis of the model’s performance on our collected images shows that common imaging pattern on a smartphone is a valuable approach for the early diagnosis. 2. Materials and Methods 2.1. Image-Capturing Method The focal length of the main camera in a smartphone is commonly short, for example, the iPhone 12 has a wide-angle (only 26 mm) camera. The focal length number tells us how much of the scene is captured in the picture, and the lower the number is, the wider the view is. Since most lesions are relatively small, we cannot capture the photograph where the lesion occupies most areas of the image. This means that the captured image may have many irrelevant backgrounds. In addition, even with the same lesion, the size of the imaging lesion may vary with different distances between the camera and lesion or using different cameras with different focal lengths. Thus these would introduce the large variability to the system’s performance going forward, and we need to find a method to reduce this considerable variability. Although identifying oral disease is very difficult for the nonmedical expert, locating the position of oral lesions is relatively easy because the visual appearance of normal and diseased tissue is significantly different. We use the camera grid to assist in locating the lesion in the center of image and to keep each area of lesion in the images neither too small nor too big. Thus it is possible to use the fixed region of interest (ROI) method to crop the discriminative parts and filter the irrelevant backgrounds, without utilizing any region proposal methods or relying on any manually cropped methods. This particular positioning of the main object in an image is helpful to improve the performance of CNN for image recognition. We use the native phone’s camera app to capture the oral cavity image. As shown in Fig. 1, the camera grid helps us to see if the lesion is properly placed at the center for optimal balance in the shot. The operation can be easily done by a person using a hand-held smartphone camera. The basic steps are as follows. Open in a separate window Fig. 1 Illustration of data acquisition. The aspect ratio is set 4:3; the image shows a lesion at the center of the region.

Significance: Speckle noise is an inherent limitation of optical coherence tomography (OCT) images that makes clinical interpretation challenging. The recent emergence of deep learning could offer a reliable method to reduce noise in OCT images. Aim: We sought to investigate the use of deep features (VGG) to limit the effect of blurriness and increase perceptual sharpness and to evaluate its impact on the performance of OCT image denoising (DnCNN). Approach: Fifty-one macula-centered OCT pairs were used in training of the network. Another set of 20 OCT pair was used for testing. The DnCNN model was cascaded with a VGG network that acted as a perceptual loss function instead of the traditional losses of L1 and L2. The VGG network remains fixed during the training process. We focused on the individual layers of the VGG-16 network to decipher the contribution of each distinctive layer as a loss function to produce denoised OCT images that were perceptually sharp and that preserved the faint features (retinal layer boundaries) essential for interpretation. The peak signal-to-noise ratio (PSNR), edge-preserving index, and no-reference image sharpness/blurriness [perceptual sharpness index (PSI), just noticeable blur (JNB), and spectral and spatial sharpness measure (S3)] metrics were used to compare deep feature losses with the traditional losses. Results: The deep feature loss produced images with high perceptual sharpness measures at the cost of less smoothness (PSNR) in OCT images. The deep feature loss outperformed the traditional losses (L1 and L2) for all of the evaluation metrics except for PSNR. The PSI, S3, and JNB estimates of deep feature loss performance were 0.31, 0.30, and 16.53, respectively. For L1 and L2 losses performance, the PSI, S3, and JNB were 0.21 and 0.21, 0.17 and 0.16, and 14.46 and 14.34, respectively. Conclusions: We demonstrate the potential of deep feature loss in denoising OCT images. Our preliminary findings suggest research directions for further investigation.

Objective. To develop an automated system to classify the severity of hypoxic-ischaemic encephalopathy injury (HIE) in neonates from the background electroencephalogram (EEG). Approach. By combining a quadratic time–frequency distribution (TFD) with a convolutional neural network, we develop a system that classifies 4 EEG grades of HIE. The network learns directly from the two-dimensional TFD through 3 independent layers with convolution in the time, frequency, and time–frequency directions. Computationally efficient algorithms make it feasible to transform each 5 min epoch to the time–frequency domain by controlling for oversampling to reduce both computation and computer memory. The system is developed on EEG recordings from 54 neonates. Then the system is validated on a large unseen dataset of 338 h of EEG recordings from 91 neonates obtained across multiple international centres. Main results. The proposed EEG HIE-grading system achieves a leave-one-subject-out testing accuracy of 88.9% and kappa of 0.84 on the development dataset. Accuracy for the large unseen test dataset is 69.5% (95% confidence interval, CI: 65.3%–73.6%) and kappa of 0.54, which is a significant ( P < 0.001 ) improvement over a state-of-the-art feature-based method with an accuracy of 56.8% (95% CI: 51.4%–61.7%) and kappa of 0.39. Performance of the proposed system was unaffected when the number of channels in testing was reduced from 8 to 2—accuracy for the large validation dataset remained at 69.5% (95% CI: 65.5%–74.0%). Significance. The proposed system outperforms the state-of-the-art machine learning algorithms for EEG grade classification on a large multi-centre unseen dataset, indicating the potential to assist clinical decision making for neonates with HIE.

Purpose: In vivo optical imaging technologies like high-resolution microendoscopy (HRME) can image nuclei of the oral epithelium. In principle, automated algorithms can then calculate nuclear features to distinguish neoplastic from benign tissue. However, images frequently contain regions without visible nuclei, due to biological and technical factors, decreasing the data available to and accuracy of image analysis algorithms. Approach: We developed the nuclear density-confidence interval (ND-CI) algorithm to determine if an HRME image contains sufficient nuclei for classification, or if a better image is required. The algorithm uses a convolutional neural network to exclude image regions without visible nuclei. Then the remaining regions are used to estimate a confidence interval (CI) for the number of abnormal nuclei per mm 2 , a feature used by a previously developed algorithm (called the ND algorithm), to classify images as benign or neoplastic. The range of the CI determines whether the ND-CI algorithm can classify an image with confidence, and if so, the predicted category. The ND and ND-CI algorithm were compared by calculating their positive predictive value (PPV) and negative predictive value (NPV) on 82 oral biopsies with histopathologically confirmed diagnoses. Results: After excluding the images that could not be classified with confidence, the ND-CI algorithm had higher PPV (65% versus 59%) and NPV (78% versus 75%) than the ND algorithm. Conclusions: The ND-CI algorithm could improve the real-time classification of HRME images of the oral epithelium by informing the user if an improved image is required for diagnosis.

In this work, we characterize the impact of large-volume processing upon the analytical sensitivity of flow-through paper-based immunoassays. Larger sample volumes feature greater molar quantities of available analyte, but the assay design principles which would enable the rapid collection of this dilute target are ill-defined. We developed a finite-element model to explore the operating conditions under which processing large sample volumes via pressure-driven convective flow would yield an improved binding signal. Our simulation results underscore the importance of establishing a high local concentration of the analyte-binding species within the porous substrate. This elevated abundance serves to enhance the binding kinetics, matching the time scale of target capture to the period during which the sample is in contact with the test zone (i.e., the effective residence time). These findings were experimentally validated using the rcSso7d-cellulose-binding domain (CBD) fusion construct, a bifunctional binding protein which adsorbs to cellulose in high abundance. As predicted by our modeling efforts, the local concentration achieved using the rcSso7d-CBD species is uniquely enabling for sensitivity enhancement through large-volume processing. The rapid analyte depletion which occurs at this high surface density also permits the processing of large sample volumes within practical time scales and flow regimes. Using these findings, we present guidance for the optimal means of processing large sample volumes for enhanced assay sensitivity.

Purpose: Reconstruction of patient anatomy is critical to patient-specific instrument (PSI) design in total joint replacement (TJR). Conventionally, computed tomography (CT) and magnetic resonance imaging (MRI) are used to obtain the patient anatomy as they are accurate imaging modalities. However, computing anatomical landmarks from the patient anatomy for PSIs requires either high-resolution CT, increasing time of scan and radiation exposure to the patient, or longer and more expensive MRI scans. As an alternative, reconstruction from single-plane fluoroscopic x-ray provides a cost-efficient tool to obtain patient anatomical structures while allowing capture of the patient’s joint dynamics, important clinical information for TJR. Approach: We present a three-dimensional (3D) reconstruction scheme that automatically and accurately reconstructs the 3D knee anatomy from single-plane fluoroscopic x-ray based on a nonlinear statistical shape model called kernel principal component analysis. To increase robustness, we designed a hybrid energy function that integrated feature and intensity information as a similarity measure for the 3D reconstruction. Results: We evaluated the proposed method on five subjects during deep knee bending: the root-mean-square accuracy is 1.19±0.36 mm for reconstructed femur and 1.15±0.17 mm for reconstructed tibia. Conclusions: The proposed method demonstrates reliable 3D bone model reconstruction accuracy with successful elimination of prior 3D imaging and reduction of manual labor and radiation dose on patient as well as characterizing joints in motion. This method is promising for applications in medical interventions such as patient-specific arthroplasty design, surgical planning, surgical navigation, and understanding anatomical and dynamic characteristics of joints.

Emerging technologies are enabling the feasibility of new types of point-of-care diagnostic devices. A portable, multimodal microscopy platform intended for use in remote diagnostic applications is presented. Use of such a system could bring high-quality microscopy to field use for diseases such as malaria, allowing better diagnostic and surveillance information to be gathered. The microscope was designed using off-the-shelf components and a manual filter selection to generate bright-field, fluorescent, and cross-polarized images of samples mounted to microscopy slides. Design parameters for the system are discussed, and characterization is performed using standardized imaging targets, multimodal phantoms, and blood smears simulating those used in malaria diagnosis. The microscope is shown to be able to image below element 9-3 of a 1951 U.S. Air Force target, indicating that the system is capable of resolving features

Purpose. 4D-CT is routine imaging for lung cancer patients treated with stereotactic body radiotherapy. No studies have investigated optimal 4D phase selection for radiomics. We aim to determine how phase data should be used to identify prognostic biomarkers for distant failure, and test whether stability assessment is required. A phase selection approach will be developed to aid studies with different 4D protocols and account for patient differences. Methods. 186 features were extracted from the tumour and peritumour on all phases for 258 patients. Feature values were selected from phase features using four methods: (A) mean across phases, (B) median across phases, (C) 50% phase, and (D) the most stable phase (closest in value to two neighbours), coined personalised selection. Four levels of stability assessment were also analysed, with inclusion of: (1) all features, (2) stable features across all phases, (3) stable features across phase and neighbour phases, and (4) features averaged over neighbour phases. Clinical-radiomics models were built for twelve combinations of feature type and assessment method. Model performance was assessed by concordance index (c-index) and fraction of new information from radiomic features. Results. The most stable phase spanned the whole range but was most often near exhale. All radiomic signatures provided new information for distant failure prediction. The personalised model had the highest c-index (0.77), and 58% of new information was provided by radiomic features when no stability assessment was performed. Conclusion. The most stable phase varies per-patient and selecting this improves model performance compared to standard methods. We advise the single most stable phase should be determined by minimising feature differences to neighbour phases. Stability assessment over all phases decreases performance by excessively removing features. Instead, averaging of neighbour phases should be used when stability is of concern. The models suggest that higher peritumoural intensity predicts distant failure.

Significance: The use of optofluidic time-stretch flow cytometry enables extreme-throughput cell imaging but suffers from the difficulties of capturing and processing a large amount of data. As significant amounts of continuous image data are generated, the images require identification with high speed. Aim: We present an intelligent cell phenotyping framework for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm, which is able to classify obtained cell images rapidly and accurately. The applied image recognition consists of density-based spatial clustering of applications with noise outlier detection, histograms of oriented gradients combining gray histogram fused feature, and XGBoost classification. Approach: We tested the ability of this framework against other previously proposed or commonly used algorithms to phenotype two groups of cell images. We quantified their performances with measures of classification ability and computational complexity based on AUC and test runtime. The tested cell image datasets were acquired from high-throughput imaging of over 20,000 drug-treated and untreated cells with an optofluidic time-stretch microscope. Results: The framework we built beats other methods with an accuracy of over 97% and a classification frequency of 3000 cells/s. In addition, we determined the optimal structure of training sets according to model performances under different training set components. Conclusions: The proposed XGBoost-based framework acts as a promising solution to processing large flow image data. This work provides a foundation for future cell sorting and clinical practice of high-throughput imaging cytometers.

Quantitative image features that can be computed from medical images are proving to be valuable biomarkers of underlying cancer biology that can be used for assessing treatment response and predicting clinical outcomes. However, validation and eventual clinical implementation of these tools is challenging due to the absence of shared software algorithms, architectures, and the tools required for computing, comparing, evaluating, and disseminating predictive models. Similarly, researchers need to have programming expertise in order to complete these tasks. The quantitative image feature pipeline (QIFP) is an open-source, web-based, graphical user interface (GUI) of configurable quantitative image-processing pipelines for both planar (two-dimensional) and volumetric (three-dimensional) medical images. This allows researchers and clinicians a GUI-driven approach to process and analyze images, without having to write any software code. The QIFP allows users to upload a repository of linked imaging, segmentation, and clinical data or access publicly available datasets (e.g., The Cancer Imaging Archive) through direct links. Researchers have access to a library of file conversion, segmentation, quantitative image feature extraction, and machine learning algorithms. An interface is also provided to allow users to upload their own algorithms in Docker containers. The QIFP gives researchers the tools and infrastructure for the assessment and development of new imaging biomarkers and the ability to use them for single and multicenter clinical and virtual clinical trials.

In the last decades, laparoscopic surgery has become the gold standard in patients with colorectal cancer. To overcome the drawback of reduced tactile feedback, real-time tissue classification could be of great benefit. In this ex vivo study, hyperspectral imaging (HSI) was used to distinguish tumor tissue from healthy surrounding tissue. A sample of fat, healthy colorectal wall, and tumor tissue was collected per patient and imaged using two hyperspectral cameras, covering the wavelength range from 400 to 1700 nm. The data were randomly divided into a training (75%) and test (25%) set. After feature reduction, a quadratic classifier and support vector machine were used to distinguish the three tissue types. Tissue samples of 32 patients were imaged using both hyperspectral cameras. The accuracy to distinguish the three tissue types using both hyperspectral cameras was 0.88 (STD=0.13) on the test dataset. When the accuracy was determined per patient, a mean accuracy of 0.93 (STD=0.12) was obtained on the test dataset. This study shows the potential of using HSI in colorectal cancer surgery for fast tissue classification, which could improve clinical outcome. Future research should be focused on imaging entire colon/rectum specimen and the translation of the technique to an intraoperative setting.

The extraction of six standard planes in 3-D cardiac ultrasound plays an important role in clinical examination to analyze cardiac function. A guideline-based learning method for efficient and accurate standard plane extraction is proposed. A cardiac ultrasound guideline determines appropriate operation steps for clinical examinations. The idea of guideline-based learning is incorporating machine learning approaches into each stage of the guideline. First, Hough forest with hierarchical search is applied for 3-D feature point detection. Second, initial planes are determined using anatomical regularities according to the guideline. Finally, a regression forest integrated with constraints of plane regularities is applied for refining each plane. The proposed method was evaluated on a 3-D cardiac ultrasound dataset and a synthetic dataset. Compared with other plane extraction methods, it demonstrated an improved accuracy with a significantly faster running time of 0.8 s/volume. Furthermore, it showed the proposed method was robust for a range abnormalities and image qualities, which would be seen in clinical practice.