Image Retrieval Method for Multiscale Objects from Optical Colonoscopy Images

Optical colonoscopy is the most common approach to diagnosing bowel diseases through direct colon and rectum inspections. Periodic optical colonoscopy examinations are particularly important for detecting cancers at early stages while still treatable. However, diagnostic accuracy is highly dependent on both the experience and knowledge of the medical doctor. Moreover, it is extremely difficult, even for specialist doctors, to detect the early stages of cancer when obscured by inflammations of the colonic mucosa due to intractable inflammatory bowel diseases, such as ulcerative colitis. Thus, to assist the UC diagnosis, it is necessary to develop a new technology that can retrieve similar cases of diagnostic target image from cases in the past that stored the diagnosed images with various symptoms of colonic mucosa. In order to assist diagnoses with optical colonoscopy, this paper proposes a retrieval method for colonoscopy images that can cope with multiscale objects. The proposed method can retrieve similar colonoscopy images despite varying visible sizes of the target objects. Through three experiments conducted with real clinical colonoscopy images, we demonstrate that the method is able to retrieve objects of any visible size and any location at a high level of accuracy

Automatic Optical Fiber Alignment System using Genetic Algorithms

Abstract. We propose and demonstrate an automatic optical fiber alignment system using genetic algorithms. Connecting optical fibers is difficult because the connecting edges should be aligned with sub-micronmeter resolution. It, therefore, takes long time even for a human expert. Although automatic fiber alignment systems are being developed, they cannot be used practically if the degrees of freedom of fiber edges are large. To overcome this difficulty, we have developed an automatic fiber alignment system using genetic algorithms, which incorporate a special local learning method. In experiments, fiber alignment of five degrees of freedom can be completed within a few minutes, whereas it would take a human expert about half an hour.

One-second Boosting: A Simple and Cost-effective Intervention that Promotes the Optimal Allocation of Cognitive Resources

Making rational judgments is not always easy. Given that aggregation of the distributed labor force through Internet has become common, a simple and cost-effective solution is needed to improve worker performance. We tested the hypothesis that enforcing a certain decision time boosts job performance by not allowing workers to provide answers within a certain short time after presenting the task. We used the binary judgment tasks, and job performance with various enforced decision times were compared. Two behavioral experiments with physicians (N = 628) demonstrated that job performance was improved by enforcing a one-second decision time; this did not affect the cognitive load of physicians. Furthermore, it was suggested that adding a one-second decision time induced the optimal trade-off between the worker’s performance and cognitive load. Our results show that focusing on resource rationality could lead to simple and cost-effective solutions to real-world problems by boosting workers’ job performance

Diagnosis and Localization of Prostate Cancer via Automated Multiparametric MRI Equipped with Artificial Intelligence

Prostate MRI scans for pre-biopsied patients are important. However, fewer radiologists are available for MRI diagnoses, which requires multi-sequential interpretations of multi-slice images. To reduce such a burden, artificial intelligence (AI)-based, computer-aided diagnosis is expected to be a critical technology. We present an AI-based method for pinpointing prostate cancer location and determining tumor morphology using multiparametric MRI. The study enrolled 15 patients who underwent radical prostatectomy between April 2008 and August 2017 at our institution. We labeled the cancer area on the peripheral zone on MR images, comparing MRI with histopathological mapping of radical prostatectomy specimens. Likelihood maps were drawn, and tumors were divided into morphologically distinct regions using the superpixel method. Likelihood maps consisted of pixels, which utilize the cancer likelihood value computed from the T2-weighted, apparent diffusion coefficient, and diffusion-weighted MRI-based texture features. Cancer location was determined based on the likelihood maps. We evaluated the diagnostic performance by the area under the receiver operating characteristic (ROC) curve according to the Chi-square test. The area under the ROC curve was 0.985. Sensitivity and specificity for our approach were 0.875 and 0.961 (p < 0.01), respectively. Our AI-based procedures were successfully applied to automated prostate cancer localization and shape estimation using multiparametric MRI