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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2020-09-03 11:30 |
Online | Online | [Short Paper]
Automatic Segmentation of Liver Tumor in Multi- phase CT Images by Attention Mask R-CNN Ryo Hasegawa, Yutaro Iwamoto (Rits Univ.), Lanfen Lin, Hongjie Hu (Zhejiang University), Yen-Wei Chen (Rits Univ.)  MI2020-25 |
Tumor detection and segmentation are essential pretreatment steps in computer-aided diagnosis of liver tumors. In this s... [more] | MI2020-25 pp.35-38 |
| SC | 2020-05-29 15:30 |
Online | Online | [Poster Presentation]
Tumor detection from colonoscopy Whole Slice Images By Deep Learning Cherubin Mugisha, Incheon Paik (School of Computer Science and Engineering) |
Image semantic segmentation is a technique of segregating an image into many parts. The goal of this research was to use... [more] | |
| IE, IMQ, MVE, CQ (Joint) [detail] |
2020-03-06 13:25 |
Fukuoka | Kyushu Institute of Technology (Cancelled but technical report was issued) |
Detection of running area from forest road images with different image quality using deep learning Misato Ushiro, Tetsuya Higashino, Yuukou Horita (Univ. of Toyama) IMQ2019-60 IE2019-142 MVE2019-81 |
Natural disasters such as falling rocks and landslides are increasing year by year on forest roads in hilly and mountain... [more] | IMQ2019-60 IE2019-142 MVE2019-81 pp.231-234 |
| ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2020-02-27 16:35 |
Hokkaido | Hokkaido Univ. (Cancelled but technical report was issued) |
A Study on Region Segmentation of Color Laparoscopic Images after Contrast Enhancement Including Super-Resolution CNN by Image Regions Norifumi Kawabata (Tokyo Univ. of Science), Toshiya Nakaguchi (Chiba Univ.) |
As one of image pre-processing method to detect, recognize, and estimate lesion or characteristic region in medical imag... [more] | |
| MI | 2020-01-29 16:20 |
Okinawa | OKINAWAKEN SEINENKAIKAN | Surgical tool segmentation from laparoscopic images using laparoscopic image syntheses and deep learning Takuya Ozawa, Yuichiro Hayashi, Hirohisa Oda, Masahiro Oda (Nagoya Univ.), Takayuki Kitasaka (Aich Ins. of Tech.), Nobuyoshi Takeshita, Masaaki Ito (NCC East), Kensaku Mori (Nagoya Univ.) MI2019-94 |
This paper proposes a surgical tool segmentation method from laparoscopic images using image synthesis and deep learning... [more] | MI2019-94 pp.129-134 |
| MI | 2020-01-30 13:25 |
Okinawa | OKINAWAKEN SEINENKAIKAN | 2D Deep CNN for automated multi organ segmentation from CT images by using consecutive slices feature maps Hiroki Isakari, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2019-113 |
The development of a computer-aided diagnosis system is expected to reduce the burden on the radiologist in clinical pra... [more] | MI2019-113 pp.203-205 |
| NC, MBE | 2019-12-06 13:25 |
Aichi | Toyohashi Tech | Proposal of Region Segmentation Algorithm for Facial Thermal Image Using Eigenfaces Yuki Hasumi, Kosuke Oiwa, Akio Nozawa (Aoyama Gakuin Univ.) MBE2019-62 NC2019-53 |
In late years, facial skin temperature acquired non-catalytically by using thermal cameras is suggested as one of the no... [more] | MBE2019-62 NC2019-53 pp.101-105 |
| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 10:35 |
Okayama | [Short Paper]
Dynamic PET Image Reconstruction using Non-Negative Matrix Decomposition with Deep Image Prior Tomoshige Shimomura, Kazuya Kawai (NIT), Muneyuki Sakata (Tokyo Metro. Inst. Gerontology), Yuichi Kimura (KU), Tatsuya Yokota, Hidekata Hontani (NIT) PRMU2019-24 MI2019-43 |
We present a PET image reconstruction method that can reconstruct dynamic PET images with high SN ratio and can simultan... [more] | PRMU2019-24 MI2019-43 pp.69-70 |
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| MVE | 2019-08-29 11:20 |
Aichi | Severity Index Evaluation using Automatic Region of Interest Segmentation Patinya Tantawiwat, Datchakorn Tancharoen (PIM), Toshihiko Yamasaki (UTokyo) MVE2019-5 |
Psoriasis Area and Severity Index (PASI) is currently a standard approach to quantify severity of psoriasis. In this pap... [more] | MVE2019-5 pp.13-16 |
|
| MI | 2019-07-06 11:55 |
Hokkaido | Future Univ. Hakodate | [Poster Presentation]
Basic study of left atrial appendage segmentation from cardiac CT images Itaru Takayashiki, Doi Akio, Toru Kato, Hiroki Takahashi (Iwate Prefectural Univ.), Shoto Sekimura (ISP), Maiko Hozawa, Yoshihiro Morino (Iwate Medical Univ.) MI2019-30 |
In this study, we propose a method to automatically extract the left atrial appendage region from the cardiac CT image f... [more] | MI2019-30 pp.43-48 |
| EST | 2019-05-17 11:20 |
Aichi | Nagoya Inst. Tech. | Personalized head models from MRI using convolutional neural networks Essam Rashed, Jose Gomez-Tames, Akimasa Hirata (NITech) EST2019-3 |
Transcranial magnetic stimulation (TMS) is a non-invasive clinical technique used for treatment of several neurological ... [more] | EST2019-3 pp.9-12 |
| NC, MBE (Joint) |
2019-03-04 15:45 |
Tokyo | University of Electro Communications | Variational Bayes algorithm of region base coupled MRF with hidden phase variables Naoki Wada (Tokyo Inst. of Tech.), Masaichiro Mizumaki (JASRI), Yoshiki Seno (Saga prefectural regional industry support center), Masato Okada (The Univ. of Tokyo), Akai Ichiro (Kumamoto Univ.), Toru Aonishi (Tokyo Inst. of Tech.) NC2018-59 |
There are two methods in coupled Markov Random Field(MRF) model for image segmentation: edge-based method and region-bas... [more] | NC2018-59 pp.87-92 |
| ITS, IE, ITE-MMS, ITE-HI, ITE-ME, ITE-AIT [detail] | 2019-02-19 16:30 |
Hokkaido | Hokkaido Univ. | A Fundamental Study on Laparoscopic Image Region Segmentation Based on Texture Analysis by Regions Norifumi Kawabata (Nagoya Univ.), Toshiya Nakaguchi (Chiba Univ.) |
Most of image region segmentation studies can be divided to both subjective method by assessors and objective method by ... [more] | |
| MI | 2019-01-22 13:20 |
Okinawa | [Short Paper]
Differences of Segmentation Results by Three Training Data for Cartilage Extraction in Knee MR Images Using Deep Learning Ryoma Aoki, Takeshi Hara (Gifu Univ), Taiki Nozaki, Masaki Matsusako (Dept.of Radiol.,St.Luke's Hosp.), Xiangrong Zhou, Hiroshi Fujita (Gifu Univ) MI2018-76 |
Accurate grasp of cartilage area is important for diagnosis and treatment related to arthropathy diseases. In recent yea... [more] | MI2018-76 pp.63-64 |
|
| MI | 2019-01-22 13:20 |
Okinawa | Cell image segmentation by Attention module Yuki Hiramatsu, Kazuhiro Hotta (Meijo Univ.) MI2018-79 |
(To be available after the conference date) [more] | MI2018-79 pp.77-80 |
|
| MI | 2019-01-22 15:35 |
Okinawa | Segmentation of lung nodules on 3D CT images by using DeconvNet and V-Net Shunsuke Kidera, Shoji Kido, Yasushi Hirano (Yamaguchi Univ.), Nobuyuki Tanaka (Saiseikai Hosp) MI2018-85 |
Semantic segmentation of lung nodules is important for texture analysis． However， manual segmentation needs a lot of tim... [more] | MI2018-85 pp.103-106 |
|
| HCGSYMPO (2nd) |
Mie | Sinfonia Technology Hibiki Hall Ise | Conversion of floor plan images to graph structures using semantic segmentation Mantaro Yamada, Toshihiko Yamasaki, Kiyoharu Aizawa (UTokyo) |
In this research, we propose a method that converts floor plan images to graph structures describing rooms’ connection. ... [more] | ||
| SIP, EA, SP, MI (Joint) [detail] |
2018-03-19 13:40 |
Okinawa | MI2017-78 |
Diseases appearing in the spine include spondylolysis, spondylolisthesis, vertebral fracture, and the like. A preoperati... [more] | MI2017-78 pp.43-44 |
|
| MBE, NC (Joint) |
2018-03-13 10:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Yuma Saito, Tsubasa Ito (Tokyo Tech), Keisuke Ota, Masanori Murayama (RIKEN), Toru Aonishi (Tokyo Tech) NC2017-68 |
Recent rapid progress of imaging techniques such as two-photon microscopes causes the extreme increase in amount of acqu... [more] | NC2017-68 pp.3-8 |
| MBE, NC (Joint) |
2018-03-13 11:15 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Application of U-Net to spine image extraction in CT image Mikoto Kamata, Masayuki Kikuchi (Tokyo Univ.of Tech.), Hayaru Shouno (Univ. of Electro-Communications.), Isao Hayashi (Kansai Univ.), Kunihiko Fukushima (Fuzzy Logic Systems Inst.) NC2017-81 |
In this study, we aimed at automatic extraction of spinal parts in CT images using deep learning as a foothold for autom... [more] | NC2017-81 pp.81-84 |
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