Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Yoshiro Kitamura"'
Autor:
Kosuke Okada, Norio Yamada, Kiyoko Takayanagi, Yuta Hiasa, Yoshiro Kitamura, Yutaka Hoshino, Susumu Hirao, Takashi Yoshiyama, Ikushi Onozaki, Seiya Kato
Publikováno v:
Tropical Medicine and Health, Vol 52, Iss 1, Pp 1-10 (2024)
Abstract Background Artificial intelligence-based computer-aided detection (AI–CAD) for tuberculosis (TB) has become commercially available and several studies have been conducted to evaluate the performance of AI–CAD for pulmonary tuberculosis (
Externí odkaz:
https://doaj.org/article/30ed4a9dfa42403ba1426c25c4aa2a93
Autor:
Shingo Iwano, Shinichiro Kamiya, Rintaro Ito, Akira Kudo, Yoshiro Kitamura, Keigo Nakamura, Shinji Naganawa
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract An artificial intelligence (AI) system that reconstructs virtual 3D thin-section CT (TSCT) images from conventional CT images by applying deep learning was developed. The aim of this study was to investigate whether virtual and real TSCT cou
Externí odkaz:
https://doaj.org/article/403657f260dd45b79f36dd16d1a23ede
Autor:
Atsushi Nakamoto, Masatoshi Hori, Hiromitsu Onishi, Takashi Ota, Hideyuki Fukui, Kazuya Ogawa, Jun Masumoto, Akira Kudo, Yoshiro Kitamura, Shoji Kido, Noriyuki Tomiyama
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-8 (2022)
Abstract Virtual thin-slice (VTS) technique is a generative adversarial network-based algorithm that can generate virtual 1-mm-thick CT images from images of 3–10-mm thickness. We evaluated the performance of VTS technique for assessment of the spi
Externí odkaz:
https://doaj.org/article/c04dfeb9cbc340d98886c12cab9eb36b
Autor:
Yuki Kataoka, Tomohisa Baba, Tatsuyoshi Ikenoue, Yoshinori Matsuoka, Junichi Matsumoto, Junji Kumasawa, Kentaro Tochitani, Hiraku Funakoshi, Tomohiro Hosoda, Aiko Kugimiya, Michinori Shirano, Fumiko Hamabe, Sachiyo Iwata, Yoshiro Kitamura, Tsubasa Goto, Shingo Hamaguchi, Takafumi Haraguchi, Shungo Yamamoto, Hiromitsu Sumikawa, Koji Nishida, Haruka Nishida, Koichi Ariyoshi, Hiroaki Sugiura, Hidenori Nakagawa, Tomohiro Asaoka, Naofumi Yoshida, Rentaro Oda, Takashi Koyama, Yui Iwai, Yoshihiro Miyashita, Koya Okazaki, Kiminobu Tanizawa, Tomohiro Handa, Shoji Kido, Shingo Fukuma, Noriyuki Tomiyama, Toyohiro Hirai, Takashi Ogura
Publikováno v:
Annals of Clinical Epidemiology. 4:110-119
[BACKGROUND] We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR). [METHODS] We used 2, 928 imag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f5df21f02a56391234038cea8dc7691
https://doi.org/10.37737/ace.22014
https://doi.org/10.37737/ace.22014
Autor:
Yujin Kudo, Yoshihisa Shimada, Jun Matsubayashi, Yoshiro Kitamura, Yojiro Makino, Sachio Maehara, Masaru Hagiwara, Jinho Park, Takafumi Yamada, Susumu Takeuchi, Masatoshi Kakihana, Toshitaka Nagao, Tatsuo Ohira, Jun Masumoto, Norihiko Ikeda
Publikováno v:
European Journal of Cardio-Thoracic Surgery. 61:751-760
OBJECTIVES Indications of limited resection, such as segmentectomy, have recently been reported for patients with solid-predominant lung cancers ≤2 cm. This study aims to identify unfavourable prognostic factors using three-dimensional imaging anal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93d4243cdf950f90ad05bbc6a358cbaf
https://doi.org/10.1093/ejcts/ezab541
https://doi.org/10.1093/ejcts/ezab541
Autor:
Saeko Sasuga, Akira Kudo, Yoshiro Kitamura, Satoshi Iizuka, Edgar Simo-Serra, Atsushi Hamabe, Masayuki Ishii, Ichiro Takemasa
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031170263
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::832ec83efae8ea3f729a23806912a9d7
https://doi.org/10.1007/978-3-031-17027-0_1
https://doi.org/10.1007/978-3-031-17027-0_1
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597245
MICCAI (6)
MICCAI (6)
Reconstructing Portal Vein and Hepatic Vein trees from contrast enhanced abdominal CT scans is a prerequisite for preoperative liver surgery simulation. Existing deep learning based methods treat vascular tree reconstruction as a semantic segmentatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9828bf63b8885e14beb75f5bdb91c3c
https://doi.org/10.1007/978-3-030-59725-2_2
https://doi.org/10.1007/978-3-030-59725-2_2
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597245
MICCAI (6)
MICCAI (6)
This paper presents a method for automatic segmentation, localization, and identification of vertebrae in arbitrary 3D CT images. Many previous works do not perform the three tasks simultaneously even though requiring a priori knowledge of which part
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7bde0b7db5aa315e5498d07bd1668b78
https://doi.org/10.1007/978-3-030-59725-2_66
https://doi.org/10.1007/978-3-030-59725-2_66
Autor:
Changhee Han, Kazuki Umemoto, Akimichi Ichinose, Hideki Nakayama, Yoshiro Kitamura, Leonardo Rundo, Yujiro Furukawa, Akira Kudo, Yuanzhong Li
Publikováno v:
3DV
Accurate Computer-Assisted Diagnosis, relying on large-scale annotated pathological images, can alleviate the risk of overlooking the diagnosis. Unfortunately, in medical imaging, most available datasets are small/fragmented. To tackle this, as a Dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a4d9d7ce1450f648692a3781a3f92d6
Publikováno v:
Machine Learning for Medical Image Reconstruction ISBN: 9783030338428
MLMIR@MICCAI
MLMIR@MICCAI
Many CT slice images are stored with large slice intervals to reduce storage size in clinical practice. This leads to low resolution perpendicular to the slice images (i.e., z-axis), which is insufficient for 3D visualization or image analysis. In th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd31aca7b8c0cf96920a46734616ddf1
https://doi.org/10.1007/978-3-030-33843-5_9
https://doi.org/10.1007/978-3-030-33843-5_9