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Experiences of virtual reality (VR) can easily break if the method of evaluating subjective user states is intrusive. Behavioral measures are increasingly used to avoid this problem. One such measure is eye tracking, which recently became more standard in VR and is often used for content-dependent analyses. This research is an endeavor to utilize content-independent eye metrics, such as pupil size and blinks, for identifying mental load in VR users. We generated mental load independently from visuals through auditory stimuli. We also defined and measured a new eye metric, focus offset, which seeks to measure the phenomenon of “staring into the distance” without focusing on a specific surface. In the experiment, VR-experienced participants listened to two native and two foreign language stimuli inside a virtual phone booth. The results show that with increasing mental load, relative pupil size on average increased 0.512 SDs (0.118 mm), with 57% reduced variance. To a lesser extent, mental load led to fewer fixations, less voluntary gazing at distracting content, and a larger focus offset as if looking through surfaces (about 0.343 SDs, 5.10 cm). These results are in agreement with previous studies. Overall, we encourage further research on content-independent eye metrics, and we hope that hardware and algorithms will be developed in the future to further increase tracking stability.

Abstract Background Some patients with the compression of the celiac trunk by the median arcuate ligament (MAL) suffer pancreatic artery aneurysms (PAAs) due to excessive blood flow from the superior mesenteric artery. These aneurysms are in peril because they are prone to rupture irrespective of size. Here, we present two cases of resection and reconstruction of PAAs caused by the compression of the celiac trunk by the MAL. Case presentation Patient 1 was a 44-year-old man who was first diagnosed to have a visceral artery aneurysm with a diameter of 4 cm accidentally found by ultrasound examination at a regular medical check-up. Contrast-enhanced CT revealed the compression of the celiac trunk by the MAL and a PAA originating from the first jejunal artery. First, laparoscopic excision of the MAL followed by a stent placement into the celiac trunk was performed. Although the stent was patent, the PAA still grew. The patient underwent resection and reconstruction of the PAA. Reconstruction of the pancreatic arterial arcade was needed because clamping of the inferior pancreaticoduodenal artery (IPDA) resulted in disappearance of the hepatic arterial blood flow. The follow-up CT 2 years and 9 months after the operation revealed no recurrence of aneurysms and the patent anastomosis. Patient 2 was a 68-year-old man who presented with an epigastric pain. Contrast-enhanced CT revealed the compression of the celiac trunk by the MAL and a PAA approximately 6 cm in diameter originating from the IPDA. The PAA was surrounded by a relatively low-intensity area, suggesting impending rupture of the PAA. The patient underwent resection and reconstruction of the PAA under an emergency situation. Reconstruction of the pancreatic arterial arcade was needed because clamping of the inflow IPDA resulted in disappearance of the hepatic blood flow. The follow-up CT 1 year and 8 months after the operation revealed no recurrence of aneurysms and the patent anastomosis. Conclusions Although long-term follow-up is needed, resection and reconstruction is one of the therapeutic choices for PAAs caused by the compression of the celiac trunk by the MAL in order to prevent catastrophic aneurysm rupture.

Maintenance of home appliances can be tedious. Maintenance work can be physically demanding and it is not always easy to know the cause of a malfunctioning appliance. Many users need to motivate themselves to perform maintenance work and consider it ideal for home appliances to be maintenance-free. On the other hand, pets and other living creatures can be taken care of with joy and without much pain, even if they are difficult to take care of. To alleviate the hassle associated with the maintenance of home appliances, we propose an augmented reality (AR) system to superimpose an agent over the home appliance of concern who changes their behavior according to the internal state of the appliance. Taking a refrigerator as an example, we verify whether such AR agent visualization motivates users to perform maintenance work and reduces the associated discomfort. We designed a cartoon-like agent and implemented a prototype system using a HoloLens 2, which can switch between several animations depending on the internal state of the refrigerator. Using the prototype system, a Wizard of Oz user study comparing three conditions was conducted. We compared the proposed method (Animacy condition), an additional behavior method (Intelligence condition), and a text-based method as a baseline for presenting the refrigerator state. In the Intelligence condition, the agent looked at the participants from time to time as if it was aware of them and exhibited help-seeking behavior only when it was considered that they could take a short break. The results show that both the Animacy and Intelligence conditions induced animacy perception and a sense of intimacy. It was also evident that the agent visualization made the participants feel more pleasant. On the other hand, the sense of discomfort was not reduced by the agent visualization and the Intelligence condition did not improve the perceived intelligence or the sense of coercion further compared to the Animacy condition.

In navigation, deep learning for inertial odometry (IO) has recently been investigated using data from a low-cost IMU only. The measurement of noise, bias, and some errors from which IO suffers is estimated with a deep neural network (DNN) to achieve more accurate pose estimation. While numerous studies on the subject highlighted the performances of their approach, the behavior of data-driven IO with DNN has not been clarified. Therefore, this paper presents a quantitative analysis of kinematics-mimicking DNN-based IO from various aspects. First, the new network architecture is designed to mimic the kinematics and ensure comprehensive analyses. Next, the hyper-parameters of neural networks that are highly correlated to IO are identified. Besides, their role in the performances is investigated. In the evaluation, the analyses were conducted with publicly-available IO datasets for vehicles and drones. The results are introduced to highlight the remaining problems in IO and are considered a guideline to promote further research.

Abstract Background Appendectomy for acute appendicitis (AA) is considered one of the most common emergency surgeries. However, emergency appendectomy accompanied with complex lesions such as extensive abscess formation is not recommended in most cases. Therefore, non-operative management followed by interval appendectomy (IA) for AA has been tried. Herein, we present three AA cases with specific etiology that underwent interval appendectomy. Case presentation Case 1: A 68-year-old man was diagnosed AA with intestinal malrotation and intra-abdominal abscesses. He initially treated with conservative therapy and underwent laparoscopic IA after detailed preoperative examination. Case 2: A 22-year-old man had been under treatment for pancolitis-type ulcerative colitis (UC), also bothered by right-lower abdominal pain several times a year. The appendix always appeared swollen on every CT taken during symptoms. He underwent laparoscopic IA; pathological finding revealed typical UC histological features in the resected appendix. After the surgery, he never suffered from terrible right lower abdominal pain. Case 3: A 69-year-old woman complaining a right lower abdominal pain had undergone CT examination, which revealed AA with appendiceal mass, irregular wall thickness of the cecum, and mediastinal and para-aortic lymph node swelling. The operation was carried out after conservative therapy. The pathological diagnosis revealed BRAF mutated colorectal carcinoma. She had received systematic chemotherapy after the surgery, and all metastatic lesions have completely disappeared. Conclusion Interval appendectomy provided us with much clearer anatomical information and precise therapeutic strategies, avoiding technical and general operative complications, and also induced fast recovery and short length of hospital stay. Interval appendectomy is a reasonable procedure and could be recommended in case of AA with some different etiology.

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m2 outdoors and and 6000 m2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future.

AIM:The clinical impact of pathological classification based on architectural pattern in hepatocellular carcinoma (HCC) remains elusive in spite of its well-known and common feature. METHODS:The prognostic impact of pathological classification was examined with prospective database. Three hundred and eighty HCC patients who underwent curative hepatectomy as an initial treatment in Kumamoto University were enrolled as a test cohort. The outcome was confirmed with a validation cohort in Kyushu University. RESULTS:Macrotrabecular (macro-T) subtype (n = 38) and compact subtype (n = 43) showed similar biological and prognostic features. Both showed higher AFP level and worse overall survival than microrabecular (micro-T) subtype (n = 266). Multivariate analysis for overall survival revealed that DCP ≥ 40, multiple tumor and macro-T/compact subtype were associated with poor overall survival (risk ratio = 2.2, 1.6 and 1.6; p = 0.002, 0.020, and 0.047, respectively). Of note, 32% of macro-T/compact subtype showed early recurrence within 1 year, which showed substantially low (5%) 5 year overall survival, whereas 16% of micro-T/PG subtype did. Twenty-one percent of macro-T/compact subtype showed multiple intrahepatic metastases (≥ 4) or distant metastases, which resulted in non-curative treatment, whereas 5% of micro-T/PG subtype did. In validation cohort, macro-T/compact subtype was an independent predictor of worse overall survival. CONCLUSION:Macro-T/compact subtype is biologically discriminated from micro-T and PG subtypes due to its aggressive features and poor prognosis after curative treatment. Additional treatment with curative hepatectomy on Macro-T/compact subtype should be discussed because of high possibility of systemic residual cancer cell.

This paper presents an end-to-end learning framework for performing 6-DOF odometry by using only inertial data obtained from a low-cost IMU. The proposed inertial odometry method allows leveraging inertial sensors that are widely available on mobile platforms for estimating their 3D trajectories. For this purpose, neural networks based on convolutional layers combined with a two-layer stacked bidirectional LSTM are explored from the following three aspects. First, two 6-DOF relative pose representations are investigated: one based on a vector in the spherical coordinate system, and the other based on both a translation vector and an unit quaternion. Second, the loss function in the network is designed with the combination of several 6-DOF pose distance metrics: mean squared error, translation mean absolute error, quaternion multiplicative error and quaternion inner product. Third, a multi-task learning framework is integrated to automatically balance the weights of multiple metrics. In the evaluation, qualitative and quantitative analyses were conducted with publicly-available inertial odometry datasets. The best combination of the relative pose representation and the loss function was the translation and quaternion together with the translation mean absolute error and quaternion multiplicative error, which obtained more accurate results with respect to state-of-the-art inertial odometry techniques.

The urban environments represent challenging areas for handheld device pose estimation (i.e., 3D position and 3D orientation) in large displacements. It is even more challenging with low-cost sensors and computational resources that are available in pedestrian mobile devices (i.e., monocular camera and Inertial Measurement Unit). To address these challenges, we propose a continuous pose estimation based on monocular Visual Odometry. To solve the scale ambiguity and suppress the scale drift, an adaptive pedestrian step lengths estimation is used for the displacements on the horizontal plane. To complete the estimation, a handheld equipment height model, with respect to the Digital Terrain Model contained in Geographical Information Systems, is used for the displacement on the vertical axis. In addition, an accurate pose estimation based on the recognition of known objects is punctually used to correct the pose estimate and reset the monocular Visual Odometry. To validate the benefit of our framework, experimental data have been collected on a 0.7 km pedestrian path in an urban environment for various people. Thus, the proposed solution allows to achieve a positioning error of 1.6⁻7.5% of the walked distance, and confirms the benefit of the use of an adaptive step length compared to the use of a fixed-step length.

Demand for indoor navigation systems has been rapidly increasing with regard to location-based services. As a cost-effective choice, inertial measurement unit (IMU)-based pedestrian dead reckoning (PDR) systems have been developed for years because they do not require external devices to be installed in the environment. In this paper, we propose a PDR system based on a chest-mounted IMU as a novel installation position for body-suit-type systems. Since the IMU is mounted on a part of the upper body, the framework of the zero-velocity update cannot be applied because there are no periodical moments of zero velocity. Therefore, we propose a novel regression model for estimating step lengths only with accelerations to correctly compute step displacement by using the IMU data acquired at the chest. In addition, we integrated the idea of an efficient map-matching algorithm based on particle filtering into our system to improve positioning and heading accuracy. Since our system was designed for 3D navigation, which can estimate position in a multifloor building, we used a barometer to update pedestrian altitude, and the components of our map are designed to explicitly represent building-floor information. With our complete PDR system, we were awarded second place in 10 teams for the IPIN 2018 Competition Track 2, achieving a mean error of 5.2 m after the 800 m walking event.

This paper presents a framework of incremental 3D cuboid modeling by using the mapping results of an RGB-D camera based simultaneous localization and mapping (SLAM) system. This framework is useful in accurately creating cuboid CAD models from a point cloud in an online manner. While performing the RGB-D SLAM, planes are incrementally reconstructed from a point cloud in each frame to create a plane map. Then, cuboids are detected in the plane map by analyzing the positional relationships between the planes, such as orthogonality, convexity, and proximity. Finally, the position, pose, and size of a cuboid are determined by computing the intersection of three perpendicular planes. To suppress the false detection of the cuboids, the cuboid shapes are incrementally updated with sequential measurements to check the uncertainty of the cuboids. In addition, the drift error of the SLAM is compensated by the registration of the cuboids. As an application of our framework, an augmented reality-based interactive cuboid modeling system was developed. In the evaluation at cluttered environments, the precision and recall of the cuboid detection were investigated, compared with a batch-based cuboid detection method, so that the advantages of our proposed method were clarified.

β-catenin signaling is a major oncogenic pathway in hepatocellular carcinoma (HCC). Since β-catenin phosphorylation by glycogen synthase kinase 3β (GSK3β) and casein kinase 1ε (CK1ε) results in its degradation, mutations affecting these phosphorylation sites cause β-catenin stabilization. However, the relevance of missense mutations in non-phosphorylation sites in exon 3 remains unclear. The current study explores significance of such mutations in addition to addressing the clinical and biological implications of β-catenin activation in human HCC.Gene alteration in exon3 of CTNNB1, gene expression of β-catenin targets such as glutamate synthetase (GS), axin2, lect2 and regucalcin (RGN), and protein expression of β-catenin were examined in 125 human HCC tissues.Sixteen patients (12.8%) showed conventional missense mutations affecting codons 33, 37, 41, and 45. Fifteen additional patients (12.0%) had other missense mutations in codon 32, 34, and 35. Induction of exon3 mutation caused described β-catenin target gene upregulation in HCC cell line. Interestingly, conventional and non-phosphorylation site mutations were equally associated with upregulation of β-catenin target genes. Nuclear localization of β-catenin was associated with poor overall survival (p = 0.0461). Of these patients with nuclear β-catenin localization, loss of described β-catenin target gene upregulation showed significant poorer overall survival than others (p = 0.0001).This study suggests that both conventional and other missense mutations in exon 3 of CTNNB1 lead to β-catenin activation in human HCC. Additionally, the mechanism of nuclear β-catenin localization without upregulation of described β-catenin target genes might be of clinical importance depending on distinct mechanism.

Portal annular pancreas (PAP) is a rare variant in which the uncinate process of the pancreas extends to the dorsal surface of the pancreas body and surrounds the portal vein or superior mesenteric vein.Upon pancreaticoduodenectomy (PD), when the pancreas is cut at the neck, two cut surfaces are created. Thus, the cut surface of the pancreas becomes larger than usual and the dorsal cut surface is behind the portal vein, therefore pancreatic fistula after PD has been reported frequently. We planned subtotal stomach-preserving PD in a 45-year-old woman with underlying insulinoma of the pancreas head. When the pancreas head was dissected, the uncinate process was extended and fused to the dorsal surface of the pancreas body. Additional resection of the pancreas body 1 cm distal to the pancreas tail to the left side of the original resection line was performed. The new cut surface became one and pancreaticojejunostomy was performed as usual. No postoperative complications such as pancreatic fistula occurred. Additional resection of the pancreas body may be a standardized procedure in patients with PAP in cases of pancreas cut surface reconstruction.

Stereo matching has been solved as a supervised learning task with convolutional neural network (CNN). However, CNN based approaches basically require huge memory use. In addition, it is still challenging to find correct correspondences between images at ill-posed dim and sensor noise regions. To solve these problems, we propose Sparse Cost Volume Net (SCV-Net) achieving high accuracy, low memory cost and fast computation. The idea of the cost volume for stereo matching was initially proposed in GC-Net. In our work, by making the cost volume compact and proposing an efficient similarity evaluation for the volume, we achieved faster stereo matching while improving the accuracy. Moreover, we propose to use weight normalization instead of commonly-used batch normalization for stereo matching tasks. This improves the robustness to not only sensor noises in images but also batch size in the training process. We evaluated our proposed network on the Scene Flow and KITTI 2015 datasets, its performance overall surpasses the GC-Net. Comparing with the GC-Net, our SCV-Net achieved to: (1) reduce 73.08 % GPU memory cost; (2) reduce 61.11 % processing time; (3) improve the 3PE from 2.87 % to 2.61 % on the KITTI 2015 dataset.

This paper proposes a new approach to visualizing spatial variation of plant status in a tomato greenhouse based on farm work information operated by laborers. Farm work information consists of a farm laborer’s position and action. A farm laborer’s position is estimated based on radio wave strength measured by using a smartphone carried by the farm laborer and Bluetooth beacons placed in the greenhouse. A farm laborer’s action is recognized based on motion data measured by using smartwatches worn on both wrists of the farm laborer. As experiment, harvesting information operated by one farm laborer in a part of a tomato greenhouse is obtained, and the spatial distribution of yields in the experimental field, called a harvesting map, is visualized. The mean absolute error of the number of harvested tomatoes in each small section of the experimental field is 0.35. An interview with the farm manager shows that the harvesting map is useful for intuitively grasping the states of the greenhouse.

Deep neural network-based (DNN-based) background subtraction has demonstrated excellent performance for moving object detection. The DNN-based background subtraction automatically learns the background features from training images and outperforms conventional background modeling based on handcraft features. However, previous works fail to detail why DNNs work well for change detection. This discussion helps to understand the potential of DNNs in background subtraction and to improve DNNs. In this paper, we observe feature maps in all layers of a DNN used in our investigation directly. The DNN provides feature maps with the same resolution as that of the input image. These feature maps help to analyze DNN behaviors because feature maps and the input image can be simultaneously compared. Furthermore, we analyzed important filters for the detection accuracy by removing specific filters from the trained DNN. From the experiments, we found that the DNN consists of subtraction operations in convolutional layers and thresholding operations in bias layers and scene-specific filters are generated to suppress false positives from dynamic backgrounds. In addition, we discuss the characteristics and issues of the DNN based on our observation.

Reconstruction-based change detection methods are robust for camera motion. The methods learn reconstruction of input images based on background images. Foreground regions are detected based on the magnitude of the difference between an input image and a reconstructed input image. For learning, only background images are used. Therefore, foreground regions have larger differences than background regions. Traditional reconstruction-based methods have two problems. One is over-reconstruction of foreground regions. The other is that decision of change detection depends on magnitudes of differences only. It is difficult to distinguish magnitudes of differences in foreground regions when the foreground regions are completely reconstructed in patch images. We propose the framework of a reconstruction-based change detection method for a free-moving camera using patch images. To avoid over-reconstruction of foreground regions, our method reconstructs a masked central region in a patch image from a region surrounding the central region. Differences in foreground regions are enhanced because foreground regions in patch images are removed by the masking procedure. Change detection is learned from a patch image and a reconstructed image automatically. The decision procedure directly uses patch images rather than the differences between patch images. Our method achieves better accuracy compared to traditional reconstruction-based methods without masking patch images.

A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.