研究者詳細 - 森　健策

論文 - 森　健策

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3Dイメージと3D印刷されたオブジェクトの利用が熟達者と初心者の空間的メンタルモデル形成に及ぼす影響招待有り査読有り

前東晃礼, 三輪和久, 小田昌宏, 中村嘉彦, 森建策, 伊神剛

人工知能学会研究会資料先進的学習科学と工学研究会 79 巻 ( 0 ) 頁： 08 2017年3月

3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation 招待有り査読有り Open Access

Oda, M; Shimizu, N; Roth, HR; Karasawa, K; Kitasaka, T; Misawa, K; Fujiwara, M; Rueckert, D; Mori, K

DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT 10553 巻頁： 222 - 230 2017年

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localization) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.

DOI： 10.1007/978-3-319-67558-9_26

Web of Science

Scopus

3Dプリンタ・ユーザーインターフェイス等の最新動向招待有り

森健策

インナービジョン 32 巻頁： 44 - 45 2017年

3Dプリンタの医療応用招待有り

森健策

医用画像情報学会雑誌 34 巻 ( 1 ) 頁： 1 - 6 2017年

3Dプリンタの医療応用招待有り

森健策

医用画像情報学会雑誌 34 巻頁： 1 - 6 2017年

3Dプリンタ-の基礎と医療応用招待有り

森健策

月刊心臓 49 巻頁： 1104 - 1113 2017年

Automatic Segmentation of Head Anatomical Structures from Sparsely-annotated Images 招待有り査読有り

Sugino Takaaki, Roth Holger R., Eshghi Mohammad, Oda Masahiro, Chung Min Suk, Mori Kensaku

2017 IEEE INTERNATIONAL CONFERENCE ON CYBORG AND BIONIC SYSTEMS (CBS) 頁： 145 - 149 2017年

Computer-Aided Diagnosis of Mammographic Masses Using Geometric Verification-Based Image Retrieval 招待有り査読有り

Li Qingliang, Shi Weili, Yang Huamin, Zhang Huimao, Li Guoxin, Chen Tao, Mori Kensaku, Jiang Zhengang

MEDICAL IMAGING 2017: COMPUTER-AIDED DIAGNOSIS 10134 巻 2017年

Comparison of the Deep-Learning-Based Automated Segmentation Methods for the Head Sectioned Images of the Virtual Korean Human Project 招待有り査読有り

Eshghi Mohammad, Roth Holger R., Oda Masahiro, Chung Min Suk, Mori Kensaku

PROCEEDINGS OF THE FIFTEENTH IAPR INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS - MVA2017 頁： 290 - 293 2017年

A study on improvement of airway segmentation using Hybrid method 招待有り査読有り

Qier M., Kitasaka T., Nimura Y., Oda M., Mori K.

Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 頁： 549 - 553 2016年6月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015

This paper presents a method for extracting an airway region from 3D chest CT volumes that uses a combination of tube enhancement filters, voxel classification based on machine learning methods and graph-cut algorithm. Lots of previous methods utilize region growing or level set algorithms without any prior knowledge of bronchi, which always fail when they reach to the peripheral bronchi. In this paper, a method of extraction based on airway shape and machine learning is proposed. The proposed method detects candidate voxels of bronchial regions by using two types of enhancement filters, and a classifier model is built for selecting the proper candidates regions based on intensity and shape features and finally the selected candidate voxels are connected by graph-cut algorithm. We applied this method on six cases of 3D chest CT volumes. The results show that this method can extract the smaller airway branches without leaking into the lung parenchyma areas.

DOI： 10.1109/ACPR.2015.7486563

Scopus

Characterization of Colorectal Lesions Using a Computer-Aided Diagnostic System for Narrow-Band Imaging Endocytoscopy 招待有り査読有り

Misawa Masashi, Kudo Shin-ei, Mori Yuichi, Nakamura Hiroki, Kataoka Shinichi, Maeda Yasuharu, Kudo Toyoki, Hayashi Takemasa, Wakamura Kunihiko, Miyachi Hideyuki, Katagiri Atsushi, Baba Toshiyuki, Ishida Fumio, Inoue Haruhiro, Nimura Yukitaka, Mori Kensaku

GASTROENTEROLOGY 150 巻 ( 7 ) 頁： 1531 - + 2016年6月

3Dイメージと3D印刷されたオブジェクトの利用が肝構造のメンタルモデル形成に与える影響招待有り査読有り

前東晃礼, 三輪和久, 小田昌宏, 中村嘉彦, 森建策, 伊神剛

人工知能学会研究会資料先進的学習科学と工学研究会 76 巻 ( 0 ) 頁： 14 2016年3月

Cascade registration of micro CT volumes taken in multiple resolutions 招待有り査読有り

Nagara K., Oda H., Nakamura S., Oda M., Homma H., Takabatake H., Mori M., Natori H., Rueckert D., Mori K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9805 LNCS 巻頁： 269 - 280 2016年

In this paper, we present a preliminary report of a multiscale registration method between micro-focus X-ray CT (micro CT) volumes taken in different scales. 3D fine structures of target objects can be observed on micro CT volumes, which are difficult to observe on clinical CT volumes. Micro CT scanners can scan specimens in various resolutions. In their high resolution volumes, ultra fine structures of specimens can be observed, while scanned areas are limited to very small. On the other hand, in low resolution volumes, large areas can be captured, while fine structures of specimens are difficult to observe. The fusion volume of the high and low resolution volumes will have benefits of both. Because the difference of resolutions between the high and low resolution volumes may vary greatly, an intermediate resolution volume is required for successful fusion of volumes. To perform such volume fusion, a cascade multi-resolution registration technique is required. To register micro CT volumes that have quite different resolutions, we employ a cascade co-registration technique. In the cascade co-registration process, intermediate resolution volumes are used in a registration process of the high and low resolution volumes. In the registration between two volumes, we apply two steps registration techniques. In the first step, a block division is used to register two resolution volumes. Afterward, we estimate the fine spatial positions relating the registered two volumes using the Powell method. The registration result can be used to generate a fusion volume of the high and low resolution volumes.

DOI： 10.1007/978-3-319-43775-0_24

Scopus

3Dプリンタ・ユーザーインターフェイス等の最新動向招待有り

森健策

インナービジョン - 巻頁： 44 - 45 2016年

3Dプリンティングのハンドリングのノウハウ招待有り

森健策

インナービジョン 31 巻頁： 20 - 24 2016年

Automated anatomical labeling of abdominal arteries and hepatic portal system extracted from abdominal CT volumes 招待有り査読有り

Matsuzaki Tetsuro, Oda Masahiro, Kitasaka Takayuki, Hayashi Yuichiro, Misawa Kazunari, Mori Kensaku

MEDICAL IMAGE ANALYSIS 20 巻 ( 1 ) 頁： 152 - 161 2015年2月

Automated Torso Organ Segmentation from 3D CT Images using Conditional Random Field 招待有り査読有り

Nimura Yukitaka, Hayashi Yuichiro, Kitasaka Takayuki, Misawa Kazunari, Mori Kensaku

MEDICAL IMAGING 2016: COMPUTER-AIDED DIAGNOSIS 9785 巻 2015年

3Dプリンティングの現状と将来展望:医用画像処理と3Dプリンタによる臓器実体モデル作成とその利用招待有り

森健策

光技術コンタクト 53 巻頁： 20 - 27 2015年

A study on improvement of airway segmentation using Hybrid method 招待有り査読有り

Qier, M; Kitasaka, T; Nimura, Y; Oda, M; Mori, K

PROCEEDINGS 3RD IAPR ASIAN CONFERENCE ON PATTERN RECOGNITION ACPR 2015 頁： 549 - 553 2015年

Automated branching pattern report generation for laparoscopic surgery assistance 招待有り査読有り

Oda Masahiro, Matsuzaki Tetsuro, Hayashi Yuichiro, Kitasaka Takayuki, Misawa Kazunari, Mori Kensaku

MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS 9414 巻 2015年

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