Isomorphic Mesh Generation from Point Clouds with Multilayer Perceptrons.

A novel neural network called the isomorphic mesh generator (iMG) is proposed to generate isomorphic meshes from point clouds containing noise and missing parts. Isomorphic meshes of arbitrary objects exhibit a unified mesh structure, despite objects belonging to different classes. This unified representation enables various modern deep neural networks (DNNs) to easily handle surface models without requiring additional pre-processing. Additionally, the unified mesh structure of isomorphic meshes enables the application of the same process to all isomorphic meshes, unlike general mesh models, where processes need to be tailored depending on their mesh structures. Therefore, the use of isomorphic meshes can ensure efficient memory usage and reduce calculation time. Apart from the point cloud of the target object used as input for the iMG, point clouds and mesh models need not be prepared in advance as training data because the iMG is a data-free method. Furthermore, the iMG outputs an isomorphic mesh obtained by mapping a reference mesh to a given input point cloud. To stably estimate the mapping function, a step-by-step mapping strategy is introduced. This strategy enables flexible deformation while simultaneously maintaining the structure of the reference mesh. Simulations and experiments conducted using a mobile phone have confirmed that the iMG reliably generates isomorphic meshes of given objects, even when the input point cloud includes noise and missing parts.

Artificial Intelligence-Based Prediction of Recurrence after Curative Resection for Colorectal Cancer from Digital Pathological Images.

BACKGROUND: To develop an artificial intelligence-based model to predict recurrence after curative resection for stage I-III colorectal cancer from digitized pathological slides. PATIENTS AND METHODS: In this retrospective study, 471 consecutive patients who underwent curative resection for stage I-III colorectal cancer at our institution from 2004 to 2015 were enrolled, and 512 randomly selected tiles from digitally scanned images of hematoxylin and eosin-stained tumor tissue sections were used to train a convolutional neural network. Five-fold cross-validation was used to validate the model. The association between recurrence and the model's output scores were analyzed in the test cohorts. RESULTS: The area under the receiver operating characteristic curve of the cross-validation was 0.7245 [95% confidence interval (CI) 0.6707-0.7783; P < 0.0001]. The score successfully classified patients into those with better and worse recurrence free survival (P < 0.0001). Multivariate analysis revealed that a high score was significantly associated with worse recurrence free survival [odds ratio (OR) 1.857; 95% CI 1.248-2.805; P = 0.0021], which was independent from other predictive factors: male sex (P = 0.0238), rectal cancer (P = 0.0396), preoperative abnormal carcinoembryonic antigen (CEA) level (P = 0.0216), pathological T3/T4 stage (P = 0.0162), and pathological positive lymph node metastasis (P < 0.0001). CONCLUSIONS: The artificial intelligence-based prediction model discriminated patients with a high risk of recurrence. This approach could help decision-makers consider the benefits of adjuvant chemotherapy.

Artificial Intelligence for Segmentation of Bladder Tumor Cystoscopic Images Performed by U-Net with Dilated Convolution.

Background: Early intravesical recurrence after transurethral resection of bladder tumors (TURBT) is often caused by overlooking of tumors during TURBT. Although narrow-band imaging and photodynamic diagnosis were developed to detect more tumors than conventional white-light imaging, the accuracy of these systems has been subjective, along with poor reproducibility due to their dependence on the physician's experience and skills. To create an objective and reproducible diagnosing system, we aimed at assessing the utility of artificial intelligence (AI) with Dilated U-Net to reduce the risk of overlooked bladder tumors when compared with the conventional AI system, termed U-Net. Materials and Methods: We retrospectively obtained cystoscopic images by converting videos obtained from 120 patients who underwent TURBT into 1790 cystoscopic images. The Dilated U-Net, which is an extension of the conventional U-Net, analyzed these image datasets. The diagnostic accuracy of the Dilated U-Net and conventional U-Net were compared by using the following four measurements: pixel-wise sensitivity (PWSe); pixel-wise specificity (PWSp); pixel-wise positive predictive value (PWPPV), representing the AI diagnostic accuracy per pixel; and dice similarity coefficient (DSC), representing the overlap area between the bladder tumors in the ground truth images and segmentation maps. Results: The cystoscopic images were divided as follows, according to the pathological T-stage: 944, Ta; 412, T1; 329, T2; and 116, carcinoma in situ. The PWSe, PWSp, PWPPV, and DSC of the Dilated U-Net were 84.9%, 88.5%, 86.7%, and 83.0%, respectively, which had improved when compared to that with the conventional U-Net by 1.7%, 1.3%, 2.1%, and 2.3%, respectively. The DSC values were high for elevated lesions and low for flat lesions for both Dilated and conventional U-Net. Conclusions: Dilated U-Net, with higher DSC values than conventional U-Net, might reduce the risk of overlooking bladder tumors during cystoscopy and TURBT.

3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning.

In the paper, we propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) in the nasopharynx from three orthogonal CT images. The proposed method introduces a cascade strategy composed of two-phase manners. In CT images, there are organs, called non-target organs, which NPC never invades. Therefore, the first phase is to detect and eliminate non-target organ regions from the CT images. In the second phase, NPC is extracted from the remained regions in the CT images. Convolutional neural networks (CNNs) are applied to detect non-target organs and NPCs. The proposed system determines the final NPC segmentation by integrating three results obtained from coronal, axial and sagittal images. Moreover, we construct two CNN-based NPC detection systems using one kind of overlapping patches with a fixed size and various overlapping patches with different sizes. From the experiments using CT images of 70 NPC patients, our proposed systems, especially the system using various patches, achieves the best performance for detecting NPC compared with conventional NPC detection methods.

Fast modified Self-organizing Deformable Model: Geometrical feature-preserving mapping of organ models onto target surfaces with various shapes and topologies.

BACKGROUND AND OBJECTIVE: This paper proposes a new method for mapping surface models of human organs onto target surfaces with the same genus as the organs. METHODS: In the proposed method, called modified Self-organizing Deformable Model (mSDM), the mapping problem is formulated as the minimization of an objective function which is defined as the weighted linear combination of four energy functions: model fitness, foldover-free, landmark mapping accuracy, and geometrical feature preservation. Further, we extend mSDM to speed up its processes, and call it Fast mSDM. RESULTS: From the mapping results of various organ models with different number of holes, it is observed that Fast mSDM can map the organ models onto their target surfaces efficiently and stably without foldovers while preserving geometrical features. CONCLUSIONS: Fast mSDM can map the organ model onto the target surface efficiently and stably, and is applicable to medical applications including Statistical Shape Model.

An informationally structured room for robotic assistance.

The application of assistive technologies for elderly people is one of the most promising and interesting scenarios for intelligent technologies in the present and near future. Moreover, the improvement of the quality of life for the elderly is one of the first priorities in modern countries and societies. In this work, we present an informationally structured room that is aimed at supporting the daily life activities of elderly people. This room integrates different sensor modalities in a natural and non-invasive way inside the environment. The information gathered by the sensors is processed and sent to a centralized management system, which makes it available to a service robot assisting the people. One important restriction of our intelligent room is reducing as much as possible any interference with daily activities. Finally, this paper presents several experiments and situations using our intelligent environment in cooperation with our service robot.

Floor sensing system using laser reflectivity for localizing everyday objects and robot.

This paper describes a new method of measuring the position of everyday objects and a robot on the floor using distance and reflectance acquired by laser range finder (LRF). The information obtained by this method is important for a service robot working in a human daily life environment. Our method uses only one LRF together with a mirror installed on the wall. Moreover, since the area of sensing is limited to a LRF scanning plane parallel to the floor and just a few centimeters above the floor, the scanning covers the whole room with minimal invasion of privacy of a resident, and occlusion problem is mitigated by using mirror. We use the reflection intensity and position information obtained from the target surface. Although it is not possible to identify all objects by additionally using reflection values, it would be easier to identify unknown objects if we can eliminate easily identifiable objects by reflectance. In addition, we propose a method for measuring the robot's pose using the tag which has the encoded reflection pattern optically identified by the LRF. Our experimental results validate the effectiveness of the proposed method.

Navigation system with real-time finite element analysis for minimally invasive surgery.

This paper presents a navigation system for minimally invasive surgery, especially laparoscopic surgery in which operates in abdomen. Conventional navigation systems show virtual images by superimposing models of target tissues on real endoscopic images. Since soft tissues within the abdomen are deformed during the surgery, the navigation system needs to provide surgeons reliable information by deforming the models according to their biomechanical behavior. However, conventional navigation systems don't consider the tissue deformation during the surgery. We have been developing a new real-time FEM-based simulation for deforming a soft tissue model by using neural network[1]. The network is called the neuroFEM. The incorporation of the neuroFEM into the navigation leads to improve the accuracy of the navigation system. In this paper, we propose a new navigation system with a framework of the neuroFEM.

A simple but accurate method for histological reconstruction of the large-sized brain tissue of the human that is applicable to construction of digitized brain database.

Research on the human brain has undoubted significance, but our knowledge on its detailed morphology is still limited. We have developed a simple method for reconstruction of large-sized brain tissues of the human. Fixed brains were cut into blocks (maximum size 7 cm x 7 cm x 1 cm), embedded and postfixed in gelatin just one overnight before obtaining complete serial sections with a vibrating microtome. Quality of stained materials was sufficient to create three-dimensional histological maps, where digital reconstructions from adjoining blocks could be accurately combined. The present method will facilitate both direct examination of the human brain and construction of its histological database.

Real-time nonlinear FEM with neural network for simulating soft organ model deformation.

This paper presents a new method for simulating the deformation of organ models by using a neural network. The proposed method is based on the idea proposed by Chen et al. that a deformed model can be estimated from the superposition of basic deformation modes. The neural network finds a relationship between external forces and the models deformed by the forces. The experimental results show that the trained network can achieve a real-time simulation while keeping the acceptable accuracy compared with the nonlinear FEM computation.