研究者詳細 - 小田　昌宏

論文 - 小田　昌宏

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TriMix: A General Framework for Medical Image Segmentation from Limited Supervision

Zheng Z., Hayashi Y., Oda M., Kitasaka T., Mori K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13846 LNCS 巻頁： 185 - 202 2023年

出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

We present a general framework for medical image segmentation from limited supervision, reducing the reliance on fully and densely labeled data. Our method is simple, jointly trains triple diverse models, and adopts a mix augmentation scheme, and thus is called TriMix. TriMix imposes consistency under a more challenging perturbation, i.e., combining data augmentation and model diversity on the tri-training framework. This straightforward strategy enables TriMix to serve as a strong and general learner learning from limited supervision using different kinds of imperfect labels. We conduct extensive experiments to show TriMix’s generic purpose for semi- and weakly-supervised segmentation tasks. Compared to task-specific state-of-the-arts, TriMix achieves competitive performance and sometimes surpasses them by a large margin. The code is available at https://github.com/MoriLabNU/TriMix.

DOI： 10.1007/978-3-031-26351-4_12

Scopus

AI画像解析による内視鏡外科手術手技のビデオ評価及び手術支援システムの構築

安井昭洋, 内田広夫, 森健策, 石田昇平, 出家亨一, 檜顕成, 城田千代栄, 小田昌宏, 林雄一郎

生体医工学 Annual61 巻 ( Abstract ) 頁： 127_2 - 127_2 2023年

記述言語：日本語出版者・発行元：公益社団法人日本生体医工学会

<p>【はじめに】術後成長発達する小児患者にとって、低侵襲手術は非常に重要である。しかし患者数は限られているため、しっかりとした手術を行うためにoff the job-training(OJT)が重要である。さらにOJTでの効率的な手技獲得には、手技を客観的に評価しfeed backを行うシステムが必須である。また安全で効率的な内視鏡手術を行うためには、臓器の位置関係の把握が必要であるため、術中ナビゲーションは重要な要件となる。これらの課題に対して、AIを用いた内視鏡手技評価および手術支援システムの構築に着手しており現状の成果を報告する。【方法と結果】食道閉鎖症モデルを用いた吻合手技を被験者に課し、各被験者の手技を最初に人の目で「check 表」「エラー項目」「時間」を用いて評価した。次にビデオから検出した鉗子の動きと人が判定した手技優劣の関係性をAIで学習させ、上位88％・下位95％の精度で手技優劣が自動判定可能となった。この結果を解析することで今まで必要だった50項目以上の肉眼チェックが、わずか７項目チェックするだけで手技の優劣を判断できることが明らかになった。現在食道閉鎖症の手術画像を用いて、食道・迷走神経・気管を深層学習させ、各種構造物の自動認識を進めている。【まとめ】AI画像解析により内視鏡手技の優劣をビデオで判定可能となった。この結果から新たに効率的な手技判断基準を定めることができた。術中ナビゲーションは現在精度のさらなる向上を目指している。</p>

DOI： 10.11239/jsmbe.annual61.127_2

CiNii Research

Multi-view Guidance for Self-supervised Monocular Depth Estimation on Laparoscopic Images via Spatio-Temporal Correspondence

Li W., Hayashi Y., Oda M., Kitasaka T., Misawa K., Mori K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 14228 LNCS 巻頁： 429 - 439 2023年

出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

This work proposes an innovative self-supervised approach to monocular depth estimation in laparoscopic scenarios. Previous methods independently predicted depth maps ignoring spatial coherence in local regions and temporal correlation between adjacent images. The proposed approach leverages spatio-temporal coherence to address the challenges of textureless areas and homogeneous colors in such scenes. This approach utilizes a multi-view depth estimation model to guide monocular depth estimation when predicting depth maps. Moreover, the minimum reprojection error is extended to construct a cost volume for the multi-view model using adjacent images. Additionally, a 3D consistency of the point cloud back-projected from predicted depth maps is optimized for the monocular depth estimation model. To benefit from spatial coherence, deformable patch-matching is introduced to the monocular and multi-view models to smooth depth maps in local regions. Finally, a cycled prediction learning for view synthesis and relative poses is designed to exploit the temporal correlation between adjacent images fully. Experimental results show that the proposed method outperforms existing methods in both qualitative and quantitative evaluations. Our code is available at https://github.com/MoriLabNU/MGMDepthL.

DOI： 10.1007/978-3-031-43996-4_41

Scopus

Masked Frequency Consistency for Domain-Adaptive Semantic Segmentation of Laparoscopic Images

Zhao X., Hayashi Y., Oda M., Kitasaka T., Mori K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 14220 LNCS 巻頁： 663 - 673 2023年

出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Semantic segmentation of laparoscopic images is an important issue for intraoperative guidance in laparoscopic surgery. However, acquiring and annotating laparoscopic datasets is labor-intensive, which limits the research on this topic. In this paper, we tackle the Domain-Adaptive Semantic Segmentation (DASS) task, which aims to train a segmentation network using only computer-generated simulated images and unlabeled real images. To bridge the large domain gap between generated and real images, we propose a Masked Frequency Consistency (MFC) module that encourages the network to learn frequency-related information of the target domain as additional cues for robust recognition. Specifically, MFC randomly masks some high-frequency information of the image to improve the consistency of the network’s predictions for low-frequency images and real images. We conduct extensive experiments on existing DASS frameworks with our MFC module and show performance improvements. Our approach achieves comparable results to fully supervised learning method on the CholecSeg8K dataset without using any manual annotation. The code is available at github.com/MoriLabNU/MFC.

DOI： 10.1007/978-3-031-43907-0_63

Scopus

ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data

Wang P., Shen C., Wang W., Oda M., Fuh C.S., Mori K., Roth H.R.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 14393 巻頁： 311 - 321 2023年

出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Developing a generalized segmentation model capable of simultaneously delineating multiple organs and diseases is highly desirable. Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data. However, the limited access to fully annotated training data poses a major challenge to training generalizable models. We propose “ConDistFL”, a framework to solve this problem by combining FL with knowledge distillation. Local models can extract the knowledge of unlabeled organs and tumors from partially annotated data from the global model with an adequately designed conditional probability representation. We validate our framework on four distinct partially annotated abdominal CT datasets from the MSD and KiTS19 challenges. The experimental results show that the proposed framework significantly outperforms FedAvg and FedOpt baselines. Moreover, the performance on an external test dataset demonstrates superior generalizability compared to models trained on each dataset separately. Our ablation study suggests that ConDistFL can perform well without frequent aggregation, reducing the communication cost of FL. Our implementation will be available at https://github.com/NVIDIA/NVFlare/tree/main/research/condist-fl.

DOI： 10.1007/978-3-031-47401-9_30

Scopus

膀胱鏡画像におけるtiny-YOLOを用いた腫瘍検出

牟田口淳, 小田昌宏, 猪口淳一, 森健策, 江藤正俊

生体医工学 Annual61 巻 ( Abstract ) 頁： 255_2 - 255_2 2023年

記述言語：日本語出版者・発行元：公益社団法人日本生体医工学会

<p>【背景】膀胱癌は経尿道手術後に再発が多い腫瘍であり、膀胱鏡での腫瘍の見落としが原因とされている。内視鏡での観察は、従来の白色光(WLI)の他に、NBIを使用するが、いずれの腫瘍検出精度は検者の技量・経験に依存するため、検査の再現性・客観性が少ないことが課題である。近年、人工知能(AI)が多くの医療分野で活用されており、AIによる検査は、客観性・再現性を持った上で、エキスパートレベルと同程度の診断能を持つ可能性があるとされている。今回、WLI/NBI膀胱鏡画像を用いて、AIによる腫瘍検出の精度を検証した。【方法】2019年から2021年まで、経尿道的膀胱腫瘍切除術(TURBT)の際に、WLI/NBIを用いて観察を行った症例の手術動画から膀胱鏡画像を作成し、腫瘍を含む画像を腫瘍画像、腫瘍を含まない画像を正常画像と定義した。腫瘍画像内の膀胱腫瘍を矩形でアノテーションを行い、テストデータ用の画像を用いてAIによる感度、特異度、陽性的中率を評価した。AIでの物体検出はtiny-YOLOを用い、腫瘍検出精度の検証を行った。【結果】WLIとNBIから、それぞれ腫瘍画像をそれぞれtiny-YOLOで学習を行い、腫瘍画像(WLI: 525枚、NBI:219枚)と正常画像(WLI:98枚、NBI:108枚)で精度検証を行った。AIによる物体検出の感度/特異度/陽性的中率は、WLIで87.8%/88.8%/97.7%、NBIで82.2%/81.4%/90.0%であった。【結論】膀胱鏡画像において、AIにより比較的良好に腫瘍検出が可能であった。更なる精度改善、リアルタイム検出への課題について、文献的考察を加え報告する。</p>

DOI： 10.11239/jsmbe.annual61.255_2

CiNii Research

Boundary-aware feature and prediction refinement for polyp segmentation 査読有り

Qiu Jie, Hayashi Yuichiro, Oda Masahiro, Kitasaka Takayuki, Mori Kensaku

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2022年12月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

Polyp segmentation from colonoscopy videos is an essential task in medical image processing for detecting early cancer. However, segmenting a precise boundary is still challenging, even with powerful deep neural networks. We consider the difficulty can be caused by: (1) the ambiguity boundary and (2) some complicated shape makes polyps hard to segment. To address these problems, we propose the Boundary-aware Feature and Prediction Refinement framework (BaFPR) for polyp segmentation. Specifically, we design a segmentation decoder for representation learning with boundary prior and propose a novel consistency loss to learn clues from the polar coordinate. The decoder mainly consists of a boundary prior module (BPM) and a bi-directional fusion module (BiFM). BPM is designed to learn the boundary prior, while BiFM learns to fuse representations of BPM and multi-scale representations from an encoder. To handle these complicated shapes of polyps, we maintain an extra segmentation network that learns with polar transformations of data to provide extra clues for the main segmentation network by our proposed consistency loss. We evaluated BaFPR with five challenging datasets for polyp segmentation and the results showed that our proposal consistently improves the segmentation performance of polyps. Code available at: https://github.com/MoriLabNU/BaFPR.

DOI： 10.1080/21681163.2022.2155579

Web of Science

Scopus

Anatomy aware-based 2.5D bronchoscope tracking for image-guided bronchoscopic navigation 査読有り

Wang Cheng, Oda Masahiro, Hayashi Yuichiro, Kitasaka Takayuki, Itoh Hayato, Honma Hirotoshi, Takebatake Hirotsugu, Mori Masaki, Natori Hiroshi, Mori Kensaku

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2022年12月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

Physicians use an endoscopic navigation system during bronchoscopy to decrease the risk of getting lost in complex tree-structure like bronchus. Most existing navigation systems based on the camera pose estimated from bronchoscope tracking and/or deep learning. However, bronchoscope tracking-based method exists tracking error, and the pre-training of the model needs massive data. This paper describes an improved bronchoscope tracking procedure by adopting image domain translation technique to improve tracking performance. Specifically, our scheme consists of three modules, an RGB-D image domain translation module, an anatomical structure classification module and a structure-aware bronchoscope tracking module. The RGB-D image domain translation module translates a real bronchoscope (RB) image to its corresponding virtual bronchoscope image and depth image. The anatomical dependency module classifies the current scene into two categories: structureless and rich structure. The bronchoscope tracking module uses a modified video-CT bronchoscope tracking approach to estimate camera pose. Experimental results showed that the proposed method achieved higher tracking accuracy than the current state-of-the-art bronchoscope tracking methods.

DOI： 10.1080/21681163.2022.2152728

Web of Science

Scopus

Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning. 査読有り

Kato S, Oda M, Mori K, Shimizu A, Otake Y, Hashimoto M, Akashi T, Hotta K

Scientific reports 12 巻 ( 1 ) 頁： 20840 2022年12月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Scientific Reports

This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the same category vary depending on the progression of the symptoms and the size of the inflamed area. In addition, it is essential that the models used be transparent and explainable, allowing health care providers to trust the models and avoid mistakes. In this study, we propose a classification method using contrastive learning and an attention mechanism. Contrastive learning is able to close the distance for images of the same category and generate a better feature space for classification. An attention mechanism is able to emphasize an important area in the image and visualize the location related to classification. Through experiments conducted on two-types of classification using a three-fold cross validation, we confirmed that the classification accuracy was significantly improved; in addition, a detailed visual explanation was achieved comparison with conventional methods.

DOI： 10.1038/s41598-022-24936-6

Scopus

PubMed

MICCAI 2022参加報告招待有り

小田昌宏

医用画像情報学会雑誌 39 巻 ( 4 ) 頁： 78 - 81 2022年12月

特集整形外科領域における人工知能の応用各論深層学習を用いたMRIでの脊髄腫瘍自動位置検出システムの構築招待有り

伊藤定之, 中島宏彰, 町野正明, 世木直喜, 小田昌宏, 大内田隼, 森下和明, 森健策, 今釜史郎

臨床整形外科 57 巻 ( 10 ) 頁： 1189 - 1195 2022年10月

A skeleton context-aware 3D fully convolutional network for abdominal artery segmentation. 査読有り

Zhu R, Oda M, Hayashi Y, Kitasaka T, Misawa K, Fujiwara M, Mori K

International journal of computer assisted radiology and surgery 2022年10月

Class-wise confidence-aware active learning for laparoscopic images segmentation. 査読有り

Qiu J, Hayashi Y, Oda M, Kitasaka T, Mori K

International journal of computer assisted radiology and surgery 2022年10月

Pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI 査読有り

Itoh H., Hu T., Oda M., Saiki S., Kamagata K., Hattori N., Aoki S., Mori K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13594 LNCS 巻頁： 63 - 75 2022年10月

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 work proposes a new feature extraction method to analyse patterns of the substantia nigra in Parkinson disease. Recent imaging techniques such that neuromelanin-sensitive MRI enable us to recognise the region of the substantia nigra and capture early Parkinson-disease-related changes. However, automated feature extraction of Parkinson-disease-related changes and their geometrical interpretation are still challenging. To tackle these challenges, we introduce a fifth-order tensor expression of multi-sequence MRI data such as T1-weighted, T2-weighted, and neuromelanin images and its tensor decomposition. Reconstruction from the selected components of the decomposition visualises the discriminative patterns of the substantia nigra between normal and Parkinson-disease patients. We collected multi-sequence MRI data from 155 patients for experiments. Using the collected data, we validate the proposed method and analyse discriminative patterns in the substantia nigra. Experimental results show that the geometrical interpretation of selected features coincides with neuropathological characteristics.

DOI： 10.1007/978-3-031-18814-5_7

Scopus

Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning 査読有り国際共著

Shen C., Wang P., Yang D., Xu D., Oda M., Chen P.T., Liu K.L., Liao W.C., Fuh C.S., Mori K., Wang W., Roth H.R.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13573 LNCS 巻頁： 58 - 67 2022年10月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Segmentation studies in medical image analysis are always associated with a particular task scenario. However, building datasets to train models to segment multiple types of organs and pathologies is challenging. For example, a dataset annotated for the pancreas and pancreatic tumors will result in a model that cannot segment other organs, like the liver and spleen, visible in the same abdominal computed tomography image. The lack of a well-annotated dataset is one limitation resulting in a lack of universal segmentation models. Federated learning (FL) is ideally suited for addressing this issue in the real-world context. In this work, we show that each medical center can use training data for distinct tasks to collaboratively build more generalizable segmentation models for multiple segmentation tasks without the requirement to centralize datasets in one place. The main challenge of this research is the heterogeneity of training data from various institutions and segmentation tasks. In this paper, we propose a multi-task segmentation framework using FL to learn segmentation models using several independent datasets with different annotations of organs or tumors. We include experiments on four publicly available single-task datasets, including MSD liver (w/ tumor), MSD spleen, MSD pancreas (w/ tumor), and KITS19. Experimental results on an external validation set to highlight the advantages of employing FL in multi-task organ and tumor segmentation.

DOI： 10.1007/978-3-031-18523-6_6

Scopus

重症COVID-19肺炎における深層学習を用いたCT画像評価と臨床パラメーターの検討査読有り

坂東皓介, 春日井大介, 後藤縁, 小田昌宏, 森健策

日本救急医学会雑誌 33 巻 ( 10 ) 頁： 760 - 760 2022年10月

特集整形外科領域における人工知能の応用各論深層学習を用いたMRIでの脊髄腫瘍自動位置検出システムの構築招待有り

伊藤定之, 中島宏彰, 町野正明, 世木直喜, 小田昌宏, 大内田隼, 森下和明, 森健策, 今釜史郎

臨床整形外科 57 巻 ( 10 ) 頁： 1189 - 1195 2022年10月

Geometric Constraints for Self-supervised Monocular Depth Estimation on Laparoscopic Images with Dual-task Consistency 査読有り

Li Wenda, Hayashi Yuichiro, Oda Masahiro, Kitasaka Takayuki, Misawa Kazunari, Mori Kensaku

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT IV 13434 巻頁： 467 - 477 2022年9月

Depth values are essential information to automate surgical robots and achieve Augmented Reality technology for minimally invasive surgery. Although depth-pose self-supervised monocular depth estimation performs impressively for autonomous driving scenarios, it is more challenging to predict accurate depth values for laparoscopic images due to the following two aspects: (i) the laparoscope’s motions contain many rotations, leading to pose estimation difficulties for the depth-pose learning strategy; (ii) the smooth surface reduces photometric error even if the matching pixels are inaccurate between adjacent frames. This paper proposes a novel self-supervised monocular depth estimation for laparoscopic images with geometric constraints. We predict the scene coordinates as an auxiliary task and construct dual-task consistency between the predicted depth maps and scene coordinates under a unified camera coordinate system to achieve pixel-level geometric constraints. We extend the pose estimation into a Siamese process to provide stronger and more balanced geometric constraints in a depth-pose learning strategy by leveraging the order of the adjacent frames in a video sequence. We also design a weight mask for depth estimation based on our consistency to alleviate the interference from predictions with low confidence. The experimental results showed that the proposed method outperformed the baseline on depth and pose estimation. Our code is available at https://github.com/MoriLabNU/GCDepthL.

DOI： 10.1007/978-3-031-16440-8_45

Web of Science

Scopus

Positive-gradient-weighted object activation mapping: visual explanation of object detector towards precise colorectal-polyp localisation 査読有り

Itoh Hayato, Misawa Masashi, Mori Yuichi, Kudo Shin-Ei, Oda Masahiro, Mori Kensaku

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2022年8月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：International Journal of Computer Assisted Radiology and Surgery

Purpose: Precise polyp detection and localisation are essential for colonoscopy diagnosis. Statistical machine learning with a large-scale data set can contribute to the construction of a computer-aided diagnosis system for the prevention of overlooking and miss-localisation of a polyp in colonoscopy. We propose new visual explaining methods for a well-trained object detector, which achieves fast and accurate polyp detection with a bounding box towards a precise automated polyp localisation. Method: We refine gradient-weighted class activation mapping for more accurate highlighting of important patterns in processing a convolutional neural network. Extending the refined mapping into multiscaled processing, we define object activation mapping that highlights important object patterns in an image for a detection task. Finally, we define polyp activation mapping to achieve precise polyp localisation by integrating adaptive local thresholding into object activation mapping. We experimentally evaluate the proposed visual explaining methods with four publicly available databases. Results: The refined mapping visualises important patterns in each convolutional layer more accurately than the original gradient-weighted class activation mapping. The object activation mapping clearly visualises important patterns in colonoscopic images for polyp detection. The polyp activation mapping localises the detected polyps in ETIS-Larib, CVC-Clinic and Kvasir-SEG database with mean Dice scores of 0.76, 0.72 and 0.72, respectively. Conclusions: We developed new visual explaining methods for a convolutional neural network by refining and extending gradient-weighted class activation mapping. Experimental results demonstrated the validity of the proposed methods by showing that accurate visualisation of important patterns and localisation of polyps in a colonoscopic image. The proposed visual explaining methods are useful for the interpreting and applying a trained polyp detector.

DOI： 10.1007/s11548-022-02696-y

Web of Science

Scopus

PubMed

テンソル分解を用いた黒質緻密部の3次元パターン表現に関する初期的検討

伊東隼人, 小田昌宏, 斉木臣二, 服部信考, 鎌形康司, 青木茂樹, 森健策

日本医用画像工学会大会予稿集 41回巻頁： 124 - 125 2022年7月

記述言語：日本語掲載種別：研究論文（研究会，シンポジウム資料等）出版者・発行元：(一社)日本医用画像工学会

黒質緻密部の3次元パターン表現に関する初期的検討について報告する.黒質緻密部における神経細胞の減少はパーキンソン病疾患者において観察される特徴のひとつである.この黒質緻密部の変化はT2強調画像や神経メラニン強調画像を介して捉えることができ,黒質緻密部の左右差や体積の減少として観察できると報告されている.一方で,得られた画像データを用いて黒質緻密部の3次元パターンの変化を計算機上で解析するためには,何らかの特徴表現が必要となる.本稿では黒質緻密部の神経メラニンMRIの信号強度比をテンソルにて表し,これらテンソルの分解に基づいた特徴表現を提案する.提案した特徴表現にて健常者・パーキンソン病患者のパターン分布を調査した結果,2つのクラスで異なる分布を表現できていることを確認した.(著者抄録)

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