Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study.

Background and study aims: Optical diagnosis of colorectal polyps is expected to improve the cost-effectiveness of colonoscopy, but achieving a high accuracy is difficult for trainees. Computer-aided diagnosis (CAD) is therefore receiving attention as an attractive tool. This study aimed to validate the efficacy of the latest CAD model for endocytoscopy (380-fold ultra-magnifying endoscopy). Patients and methods: This international web-based trial was conducted between August and November 2015. A web-based test comprising one white-light and one endocytoscopic image of 205 small colorectal polyps (≤ 10 mm) from 123 patients was undertaken by both CAD and by endoscopists (three experts and ten non-experts from three countries). Outcome measures were accuracy in identifying neoplastic change in diminutive (≤ 5 mm) and small (≤ 10 mm) polyps, and accuracy in predicting post-polypectomy surveillance intervals according to current guidelines for high confidence optical diagnoses of diminutive polyps. Results: Of the 205 small polyps (147 neoplastic and 58 non-neoplastic), 139 were diminutive. CAD was accurate for 89 % (95 % confidence interval [CI] 83 % - 94 %) of diminutive polyps and 89 % (84 % - 93 %) of small polyps, which was significantly greater than results for the non-experts (73 % [71 % - 76 %], P < 0.001; and 76 % [74 % - 78 %], P < 0.001, respectively) and comparable with the experts' results (90 % [87 % - 93 %], P = 0.703; and 91 % [89 % - 93 %], P = 0.106, respectively). The surveillance interval predicted by CAD provided 98 % (93 % - 100 %) and 96 % (91 % - 99 %) agreement with pathology-directed intervals of the European and American guidelines, respectively. Conclusions: The use of CAD in endocytoscopy can be effective in the management of diminutive/small colorectal polyps.UMIN Clinical Trial Registry: UMIN000018185.

Robust endocytoscopic image classification based on higher-order symmetric tensor analysis and multi-scale topological statistics.

PURPOSE: An endocytoscope is a new type of endoscope that enables users to perform conventional endoscopic observation and ultramagnified observation at the cell level. Although endocytoscopy is expected to improve the cost-effectiveness of colonoscopy, endocytoscopic image diagnosis requires much knowledge and high-level experience for physicians. To circumvent this difficulty, we developed a robust endocytoscopic (EC) image classification method for the construction of a computer-aided diagnosis (CAD) system, since real-time CAD can resolve accuracy issues and reduce interobserver variability. METHOD: We propose a novel feature extraction method by introducing higher-order symmetric tensor analysis to the computation of multi-scale topological statistics on an image, and we integrate this feature extraction with EC image classification. We experimentally evaluate the classification accuracy of our proposed method by comparing it with three deep learning methods. We conducted this comparison by using our large-scale multi-hospital dataset of about 55,000 images of over 3800 patients. RESULTS: Our proposed method achieved an average 90% classification accuracy for all the images in four hospitals even though the best deep learning method achieved 95% classification accuracy for images in only one hospital. In the case with a rejection option, the proposed method achieved expert-level accurate classification. These results demonstrate the robustness of our proposed method against pit pattern variations, including differences of colours, contrasts, shapes, and hospitals. CONCLUSIONS: We developed a robust EC image classification method with novel feature extraction. This method is useful for the construction of a practical CAD system, since it has sufficient generalisation ability.

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.

PURPOSE: Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree. METHODS: This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree. RESULTS: A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate. CONCLUSION: A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

Automatic anatomical labeling of arteries and veins using conditional random fields.

PURPOSE: For safe and reliable laparoscopic surgery, it is important to determine individual differences of blood vessels such as the position, shape, and branching structures. Consequently, a computer-assisted laparoscopy that displays blood vessel structures with anatomical labels would be extremely beneficial. This paper details an automated anatomical labeling method for abdominal arteries and veins extracted from 3D CT volumes. METHODS: The proposed method represents a blood vessel tree as a probabilistic graphical model by conditional random fields (CRFs). An adaptive gradient algorithm is adopted for structure learning. The anatomical labeling of blood vessel branches is performed by maximum a posteriori estimation. RESULTS: We applied the proposed method to 50 cases of arterial and portal phase abdominal X-ray CT volumes. The experimental results showed that the F-measure of the proposed method for abdominal arteries and veins was 94.4 and 86.9%, respectively. CONCLUSION: We developed an automated anatomical labeling method to annotate each blood vessel branches of abdominal arteries and veins using CRF. The proposed method outperformed a state-of-the-art method.

Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts.

PURPOSE: Real-time characterization of colorectal lesions during colonoscopy is important for reducing medical costs, given that the need for a pathological diagnosis can be omitted if the accuracy of the diagnostic modality is sufficiently high. However, it is sometimes difficult for community-based gastroenterologists to achieve the required level of diagnostic accuracy. In this regard, we developed a computer-aided diagnosis (CAD) system based on endocytoscopy (EC) to evaluate cellular, glandular, and vessel structure atypia in vivo. The purpose of this study was to compare the diagnostic ability and efficacy of this CAD system with the performances of human expert and trainee endoscopists. METHODS: We developed a CAD system based on EC with narrow-band imaging that allowed microvascular evaluation without dye (ECV-CAD). The CAD algorithm was programmed based on texture analysis and provided a two-class diagnosis of neoplastic or non-neoplastic, with probabilities. We validated the diagnostic ability of the ECV-CAD system using 173 randomly selected EC images (49 non-neoplasms, 124 neoplasms). The images were evaluated by the CAD and by four expert endoscopists and three trainees. The diagnostic accuracies for distinguishing between neoplasms and non-neoplasms were calculated. RESULTS: ECV-CAD had higher overall diagnostic accuracy than trainees (87.8 vs 63.4%; [Formula: see text]), but similar to experts (87.8 vs 84.2%; [Formula: see text]). With regard to high-confidence cases, the overall accuracy of ECV-CAD was also higher than trainees (93.5 vs 71.7%; [Formula: see text]) and comparable to experts (93.5 vs 90.8%; [Formula: see text]). CONCLUSIONS: ECV-CAD showed better diagnostic accuracy than trainee endoscopists and was comparable to that of experts. ECV-CAD could thus be a powerful decision-making tool for less-experienced endoscopists.

Pneumoperitoneum simulation based on mass-spring-damper models for laparoscopic surgical planning.

Laparoscopic surgery, which is one minimally invasive surgical technique that is now widely performed, is done by making a working space (pneumoperitoneum) by infusing carbon dioxide ([Formula: see text]) gas into the abdominal cavity. A virtual pneumoperitoneum method that simulates the abdominal wall and viscera motion by the pneumoperitoneum based on mass-spring-damper models (MSDMs) with mechanical properties is proposed. Our proposed method simulates the pneumoperitoneum based on MSDMs and Newton's equations of motion. The parameters of MSDMs are determined by the anatomical knowledge of the mechanical properties of human tissues. Virtual [Formula: see text] gas pressure is applied to the boundary surface of the abdominal cavity. The abdominal shapes after creation of the pneumoperitoneum are computed by solving the equations of motion. The mean position errors of our proposed method using 10 mmHg virtual gas pressure were [Formula: see text], and the position error of the previous method proposed by Kitasaka et al. was 35.6 mm. The differences in the errors were statistically significant ([Formula: see text], Student's [Formula: see text]-test). The position error of the proposed method was reduced from [Formula: see text] to [Formula: see text] using 30 mmHg virtual gas pressure. The proposed method simulated abdominal wall motion by infused gas pressure and generated deformed volumetric images from a preoperative volumetric image. Our method predicted abdominal wall deformation by just giving the [Formula: see text] gas pressure and the tissue properties. Measurement of the visceral displacement will be required to validate the visceral motion.

Assessment of COPD severity by combining pulmonary function tests and chest CT images.

PURPOSE: Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitations. Physicians frequently assess the stage using pulmonary function tests and chest CT images. This paper describes a novel method to assess COPD severity by combining measurements of pulmonary function tests (PFT) and the results of chest CT image analysis. METHODS: The proposed method utilizes measurements from PFTs and chest CT scans to assess COPD severity. This method automatically classifies COPD severity into five stages, described in GOLD guidelines, by a multi-class AdaBoost classifier. The classifier utilizes 24 measurements as feature values, which include 18 measurements from PFTs and six measurements based on chest CT image analysis. A total of 3 normal and 46 abnormal (COPD) examinations performed in adults were evaluated using the proposed method to test its diagnostic capability. RESULTS: The experimental results revealed that its accuracy rates were 100.0 % (resubstitution scheme) and 53.1 % (leave-one-out scheme). A total of 95.7 % of missed classifications were assigned in the neighboring severities. CONCLUSIONS: These results demonstrate that the proposed method is a feasible means to assess COPD severity. A much larger sample size will be required to establish the limits of the method and provide clinical validation.

Anatomical annotation on vascular structure in volume rendered images.

The precise annotation of vascular structure is desired in computer-assisted systems to help surgeons identify each vessel branch. This paper proposes a method that annotates vessels on volume rendered images by rendering their names on them using a two-pass rendering process. In the first rendering pass, vessel surface models are generated using such properties as centerlines, radii, and running directions. Then the vessel names are drawn on the vessel surfaces. Finally, the vessel name images and the corresponding depth buffer are generated by a virtual camera at the viewpoint. In the second rendering pass, volume rendered images are generated by a ray casting volume rendering algorithm that considers the depth buffer generated in the first rendering pass. After the two-pass rendering is finished, an annotated image is generated by blending the volume rendered image with the surface rendered image. To confirm the effectiveness of our proposed method, we performed a computer-assisted system for the automated annotation of abdominal arteries. The experimental results show that vessel names can be drawn on the corresponding vessel surface in the volume rendered images at a computing cost that is nearly the same as that by volume rendering only. The proposed method has enormous potential to be adopted to annotate the vessels in the 3D medical images in clinical applications, such as image-guided surgery.

Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images.

This paper presents an automated multi-organ segmentation method for 3D abdominal CT images based on a spatially-divided probabilistic atlases. Most previous abdominal organ segmentation methods are ineffective to deal with the large differences among patients in organ shape and position in local areas. In this paper, we propose an automated multi-organ segmentation method based on a spatially-divided probabilistic atlas, and solve this problem by introducing a scale hierarchical probabilistic atlas. The algorithm consists of image-space division and a multi-scale weighting scheme. The generated spatial-divided probabilistic atlas efficiently reduces the inter-subject variance in organ shape and position either in global or local regions. Our proposed method was evaluated using 100 abdominal CT volumes with manually traced ground truth data. Experimental results showed that it can segment the liver, spleen, pancreas, and kidneys with Dice similarity indices of 95.1%, 91.4%, 69.1%, and 90.1%, respectively.