Dr. Janne J. Näppi Profile

Dr. Janne J. Näppi

Associate Investigator at Massachusetts General Hospital

SPIE Involvement:

Conference Program Committee | Author

Area of Expertise:

computer-aided detection , computer-assisted diagnosis , machine learning , CT colonography , radiomics , breast imaging

Publications (56)

Proceedings Article | 2 April 2024 Poster + Paper

Automated segmentation of polyps by 3D deep learning in photon-counting CT colonography

Janne Näppi, Toru Hironaka, Dufan Wu, Rajiv Gupta, Rie Tachibana, Katsuyuki Taguchi, Masaki Okamoto, Hiroyuki Yoshida

Proceedings Volume 12931, 129311C (2024) https://doi.org/10.1117/12.3007290

KEYWORDS: Artificial intelligence, Image denoising, Polyps, Image segmentation, Virtual colonoscopy, 3D image processing, Colorectal cancer, Denoising, Transformers, Cancer

Read Abstract +

Proceedings Article | 2 April 2024 Poster + Paper

Electronic cleansing in CT colonography using transformer-based UNet

Rie Tachibana, Janne Näppi, Masaki Okamoto, Hiroyuki Yoshida

Proceedings Volume 12931, 129311B (2024) https://doi.org/10.1117/12.3006129

KEYWORDS: 3D image processing, Virtual colonoscopy

Read Abstract +

Proceedings Article | 2 April 2024 Presentation + Paper

3D generative AI for electronic cleansing in CT colonography

Rie Tachibana, Janne Näppi, Toru Hironaka, Masaki Okamoto, Hiroyuki Yoshida

Proceedings Volume 12931, 129310J (2024) https://doi.org/10.1117/12.3009202

KEYWORDS: 3D image processing, Virtual colonoscopy

Read Abstract +

SPIE Journal Paper | 9 October 2023 Open Access

Bayesian uncertainty estimation for detection of long-tailed and unseen conditions in medical images

Mina Rezaei, Janne Näppi, Bernd Bischl, Hiroyuki Yoshida

JMI, Vol. 10, Issue 05, 054501, (October 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.5.054501

KEYWORDS: Gallium nitride, Education and training, Image segmentation, Data modeling, Semantics, Machine learning, Performance modeling, Medical imaging, Deep learning, Statistical modeling

Read Abstract +

PurposeDeep supervised learning provides an effective approach for developing robust models for various computer-aided diagnosis tasks. However, there is often an underlying assumption that the frequencies of the samples between the different classes of the training dataset are either similar or balanced. In real-world medical data, the samples of positive classes often occur too infrequently to satisfy this assumption. Thus, there is an unmet need for deep-learning systems that can automatically identify and adapt to the real-world conditions of imbalanced data.ApproachWe propose a deep Bayesian ensemble learning framework to address the representation learning problem of long-tailed and out-of-distribution (OOD) samples when training from medical images. By estimating the relative uncertainties of the input data, our framework can adapt to imbalanced data for learning generalizable classifiers. We trained and tested our framework on four public medical imaging datasets with various imbalance ratios and imaging modalities across three different learning tasks: semantic medical image segmentation, OOD detection, and in-domain generalization. We compared the performance of our framework with those of state-of-the-art comparator methods.ResultsOur proposed framework outperformed the comparator models significantly across all performance metrics (pairwise t-test: p < 0.01) in the semantic segmentation of high-resolution CT and MR images as well as in the detection of OOD samples (p < 0.01), thereby showing significant improvement in handling the associated long-tailed data distribution. The results of the in-domain generalization also indicated that our framework can enhance the prediction of retinal glaucoma, contributing to clinical decision-making processes.ConclusionsTraining of the proposed deep Bayesian ensemble learning framework with dynamic Monte-Carlo dropout and a combination of losses yielded the best generalization to unseen samples from imbalanced medical imaging datasets across different learning tasks.

DOWNLOAD PAPER

Proceedings Article | 10 April 2023 Poster + Paper

Automated detection of colorectal polyps in photon-counting CT colonography

Janne Näppi, Toru Hironaka, Dufan Wu, Stephen Yoshida, Rajiv Gupta, Rie Tachibana, Katsuyuki Taguchi, Hiroyuki Yoshida

Proceedings Volume 12469, 124690T (2023) https://doi.org/10.1117/12.2654292

KEYWORDS: Computed tomography, Photon counting, Polyps, Virtual colonoscopy, Computer aided detection, Error control coding, Quantum noise, Photons, Ionizing radiation, Colorectal cancer, Colon, X-rays

Read Abstract +

Proceedings Article | 10 April 2023 Poster + Paper

Electronic cleansing in photon-counting CT colonography by use of self-supervised 3D-GAN

Rie Tachibana, Janne Näppi, Toru Hironaka, Stephen Yoshida, Dufan Wu, Rajiv Gupta, Hiroyuki Yoshida

Proceedings Volume 12469, 124690U (2023) https://doi.org/10.1117/12.2654341

KEYWORDS: Education and training, Computed tomography, Virtual colonoscopy, Gallium nitride, Colon, Image quality, 3D modeling, Contrast agents, Polyps, Scanners, Photon counting

Read Abstract +

Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Deep mutual GANs: representation learning from multiple experts

Mina Rezaei, Janne Näppi, Bernd Bischl, Hiroyuki Yoshida

Proceedings Volume 12037, 120370U (2022) https://doi.org/10.1117/12.2612968

KEYWORDS: Gallium nitride, Data modeling, Performance modeling, Tumor growth modeling, Medical imaging, Liver, Image segmentation, Head, Skin, Instrument modeling

Read Abstract +

Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Electronic cleansing by unpaired contrastive learning in non-cathartic laxative-free CT colonography

Janne Näppi, Rie Tachibana, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 12037, 120370S (2022) https://doi.org/10.1117/12.2611213

KEYWORDS: Virtual colonoscopy, 3D modeling, Computed tomography, Colorectal cancer, Colon, Cancer, Visualization, Gallium nitride, Statistical modeling, Forward error correction

Read Abstract +

Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Uncertainty quantification in 3D deep learning for detecting serrated polyps in CT colonography

Janne Näppi, Perry Pickhardt, David Kim, Hiroyuki Yoshida

Proceedings Volume 12037, 120370T (2022) https://doi.org/10.1117/12.2612637

KEYWORDS: Virtual colonoscopy, Artificial intelligence, 3D modeling, Convolution, Colorectal cancer, Cancer, Performance modeling, Error control coding, Data modeling, 3D image processing

Read Abstract +

Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Unsupervised 3D prediction model for pulmonary disease progression in chest CT

Janne Näppi, Tomoki Uemura, Chinatsu Watari, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 12037, 120370O (2022) https://doi.org/10.1117/12.2612375

KEYWORDS: Computed tomography, 3D modeling, Chest, Data modeling, Compound parabolic concentrators, 3D image processing, Lung, Visualization, Statistical analysis, Network architectures

Read Abstract +

Proceedings Article | 4 April 2022 Presentation + Paper

Bayesian uncertainty estimation for detection of long-tail and unseen conditions in abdominal images

Mina Rezaei, Janne Näppi, Bernd Bischl, Hiroyuki Yoshida

Proceedings Volume 12033, 1203311 (2022) https://doi.org/10.1117/12.2612955

KEYWORDS: Image segmentation, Data modeling, Medical imaging, Statistical modeling, Liver, Computed tomography, Network architectures, Magnetic resonance imaging, Statistical analysis

Read Abstract +

Proceedings Article | 15 March 2021 Poster + Presentation + Paper

U-radiomics for predicting survival of patients with COVID-19

Tomoki Uemura, Janne Näppi, Chinatsu Watari, Tohru Kamiya, Hiroyuki Yoshida

Proceedings Volume 11601, 1160110 (2021) https://doi.org/10.1117/12.2581907

KEYWORDS: Lung, Computed tomography, Tissues, Proteins, Chest

Read Abstract +

Proceedings Article | 12 March 2021 Poster + Presentation + Paper

Unsupervised survival prediction model from CT images of patients with COVID-19

Tomoki Uemura, Janne Näppi, Chinatsu Watari, Tohru Kamiya, Hiroyuki Yoshida

Proceedings Volume 11597, 1159732 (2021) https://doi.org/10.1117/12.2581929

KEYWORDS: Computed tomography, Chest, Network architectures, Statistical modeling, Proteins, Performance modeling, Lung, Computer programming

Read Abstract +

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Comparative performance of self-supervised 3D-ResNet-GAN for electronic cleansing in single- and dual-energy CT colonography

Rie Tachibana, Janne Näppi, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 11601, 1160116 (2021) https://doi.org/10.1117/12.2582013

KEYWORDS: Virtual colonoscopy, 3D modeling, Yield improvement, Visualization, Performance modeling, Convolution

Read Abstract +

Proceedings Article | 15 February 2021 Presentation + Paper

3D deep learning for computer-aided detection of serrated polyps in CT colonography

Janne Näppi, Tomoki Uemura, Perry Pickhardt, David Kim, Hiroyuki Yoshida

Proceedings Volume 11597, 115970I (2021) https://doi.org/10.1117/12.2581776

KEYWORDS: Virtual colonoscopy, Computer-aided diagnosis, Colorectal cancer, Error control coding, Computer aided diagnosis and therapy, Cancer, Coating, Image resolution, Computed tomography, 3D image processing

Read Abstract +

Proceedings Article | 17 March 2020 Paper

GAN-based survival prediction model from CT images of patients with idiopathic pulmonary fibrosis

Tomoki Uemura, Chinatsu Watari, Janne Näppi, Toru Hironaka, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11318, 113181F (2020) https://doi.org/10.1117/12.2551369

KEYWORDS: Computed tomography, Chest, Pulmonary fibrosis, Performance modeling, Network architectures, Physiology, Lung, Biological research, Image analysis, 3D modeling

Read Abstract +

We developed a novel survival prediction model for images, called pix2surv, based on a conditional generative adversarial network (cGAN), and evaluated its performance based on chest CT images of patients with idiopathic pulmonary fibrosis (IPF). The architecture of the pix2surv model has a time-generator network that consists of an encoding convolutional network, a fully connected prediction network, and a discriminator network. The fully connected prediction network is trained to generate survival-time images from the chest CT images of each patient. The discriminator network is a patchbased convolutional network that is trained to differentiate the “fake pair” of a chest CT image and a generated survivaltime image from the “true pair” of an input CT image and the observed survival-time image of a patient. For evaluation, we retrospectively collected 75 IPF patients with high-resolution chest CT and pulmonary function tests. The survival predictions of the pix2surv model on these patients were compared with those of an established clinical prognostic biomarker known as the gender, age, and physiology (GAP) index by use of a two-sided t-test with bootstrapping. Concordance index (C-index) and relative absolute error (RAE) were used as measures of the prediction performance. Preliminary results showed that the survival prediction by the pix2surv model yielded more than 15% higher C-index value and more than 10% lower RAE values than those of the GAP index. The improvement in survival prediction by the pix2surv model was statistically significant (P < 0.0001). Also, the separation between the survival curves for the low- and high-risk groups was larger with pix2surv than that of the GAP index. These results show that the pix2surv model outperforms the GAP index in the prediction of the survival time and risk stratification of patients with IPF, indicating that the pix2surv model can be an effective predictor of the overall survival of patients with IPF.

Proceedings Article | 16 March 2020 Paper

Comparative performance of 3D machine-learning and deep-learning models in the detection of small polyps in dual-energy CT colonography

Janne Näppi, Tomoki Uemura, Se Hyung Kim, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11314, 113143C (2020) https://doi.org/10.1117/12.2549793

KEYWORDS: Spectral computed tomography, Machine learning, Computed tomography, Cancer, Computer aided diagnosis and therapy, Virtual colonoscopy, Colorectal cancer, 3D image processing, Radiology

Read Abstract +

Proceedings Article | 16 March 2020 Paper

Comparative performance of 3D-DenseNet, 3D-ResNet, and 3D-VGG models in polyp detection for CT colonography

Tomoki Uemura, Janne Näppi, Toru Hironaka, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11314, 1131435 (2020) https://doi.org/10.1117/12.2549103

KEYWORDS: 3D modeling, Computer aided diagnosis and therapy, 3D image processing, Medical imaging, Performance modeling, Computed tomography, Virtual colonoscopy, Visualization, Data modeling, Solid modeling

Read Abstract +

Proceedings Article | 16 March 2020 Paper

U-radiomics for predicting survival of patients with idiopathic pulmonary fibrosis

Tomoki Uemura, Chinatsu Watari, Janne Näppi, Toru Hironaka, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11314, 113140T (2020) https://doi.org/10.1117/12.2551273

KEYWORDS: Computed tomography, Lung, Pulmonary fibrosis, Chest, Performance modeling

Read Abstract +

Proceedings Article | 16 March 2020 Paper

Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis

Mina Rezaei, Tomoki Uemura, Janne Näppi, Hiroyuki Yoshida, Christoph Lippert, Christoph Meinel

Proceedings Volume 11314, 113140E (2020) https://doi.org/10.1117/12.2551166

KEYWORDS: Medical imaging, Image classification

Read Abstract +

Proceedings Article | 12 March 2020 Paper

Self-supervised generative adversarial network for electronic cleansing in dual-energy CT colonography

Rie Tachibana, Janne Näppi, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 11318, 113181E (2020) https://doi.org/10.1117/12.2549234

KEYWORDS: Colon, Virtual colonoscopy, 3D modeling, Computed tomography, Image quality, Colorectal cancer, Visualization, Data modeling, Endoscopy, Cancer

Read Abstract +

Proceedings Article | 27 March 2019 Paper

Deep radiomic precision CT imaging for prognostic biomarkers for interstitial lung diseases

Tomoki Uemura, Mikio Matsuhiro, Chinatsu Watari, Janne Näppi, Radin Nasirudin, Toru Hironaka, Yoshiki Kawata, Noboru Niki, Hiroyuki Yoshida

Proceedings Volume 10954, 109541E (2019) https://doi.org/10.1117/12.2512058

KEYWORDS: Lung, Computed tomography, Image analysis, Performance modeling, Convolutional neural networks, Pulmonary function tests, Statistical analysis, Physiology, Gallium nitride, Feature extraction

Read Abstract +

Proceedings Article | 18 March 2019 Presentation + Paper

Ensemble 3D residual network (E3D-ResNet) for reduction of false-positive polyp detections in CT colonography

Tomoki Uemura, Janne Näppi, Huimin Lu, Hyoungseop Kim, Rie Tachibana, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 10950, 1095013 (2019) https://doi.org/10.1117/12.2512173

KEYWORDS: Performance modeling, Virtual colonoscopy, Data modeling, Statistical modeling, Gallium nitride, Computed tomography, Image processing, Convolution, 3D modeling, Computer aided diagnosis and therapy

Read Abstract +

Proceedings Article | 15 March 2019 Paper

Electronic cleansing in CT colonography using a generative adversarial network

Rie Tachibana, Janne Näppi, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 10954, 1095419 (2019) https://doi.org/10.1117/12.2512466

KEYWORDS: 3D modeling, Virtual colonoscopy, Colon, Data modeling, Computed tomography, Network architectures, Colorectal cancer, Tissues, Natural surfaces

Read Abstract +

Proceedings Article | 15 March 2019 Paper

Cycle-consistent 3D-generative adversarial network for virtual bowel cleansing in CT colonography

Janne Näppi, Hiroyuki Yoshida

Proceedings Volume 10949, 109492Z (2019) https://doi.org/10.1117/12.2512599

KEYWORDS: Virtual colonoscopy, 3D image processing, Colon, Tissues, Visualization, Colorectal cancer, Cancer, Computed tomography, 3D vision

Read Abstract +

Proceedings Article | 6 March 2018 Paper

Radiomic texture-curvature (RTC) features for precision medicine of patients with rheumatoid arthritis-associated interstitial lung disease

Chinatsu Watari, Mikio Matsuhiro, Janne Näppi, Radin Nasirudin, Toru Hironaka, Yoshiki Kawata, Noboru Niki, Hiroyuki Yoshida

Proceedings Volume 10579, 105791N (2018) https://doi.org/10.1117/12.2294043

KEYWORDS: Lung, Computed tomography, Feature extraction, Chest, Pulmonary function tests

Read Abstract +

Proceedings Article | 28 February 2018 Paper

Deep radiomic prediction with clinical predictors of the survival in patients with rheumatoid arthritis-associated interstitial lung diseases

Radin Nasirudina, Janne Näppi, Chinatsu Watari, Mikio Matsuhiro, Toru Hironaka, Shoji Kido, Hiroyuki Yoshida

Proceedings Volume 10575, 105753R (2018) https://doi.org/10.1117/12.2293374

KEYWORDS: Lung, Computed tomography, Feature extraction, Performance modeling, Pulmonary function tests, Convolutional neural networks

Read Abstract +

We developed and evaluated the effect of our deep-learning-derived radiomic features, called deep radiomic features (DRFs), together with their combination with clinical predictors, on the prediction of the overall survival of patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD). We retrospectively identified 70 RA-ILD patients with thin-section lung CT and pulmonary function tests. An experienced observer delineated regions of interest (ROIs) from the lung regions on the CT images, and labeled them into one of four ILD patterns (ground-class opacity, reticulation, consolidation, and honeycombing) or a normal pattern. Small image patches centered at individual pixels on these ROIs were extracted and labeled with the class of the ROI to which the patch belonged. A deep convolutional neural network (DCNN), which consists of a series of convolutional layers for feature extraction and a series of fully connected layers, was trained and validated with 5-fold cross-validation for classifying the image patches into one of the above five patterns. A DRF vector for each patch was identified as the output of the last convolutional layer of the DCNN. Statistical moments of each element of the DRF vectors were computed to derive a DRF vector that characterizes the patient. The DRF vector was subjected to a Cox proportional hazards model with elastic-net penalty for predicting the survival of the patient. Evaluation was performed by use of bootstrapping with 2,000 replications, where concordance index (C-index) was used as a comparative performance metric. Preliminary results on clinical predictors, DRFs, and their combinations thereof showed (a) Gender and Age: C-index 64.8% [95% confidence interval (CI): 51.7, 77.9]; (b) gender, age, and physiology (GAP index): C-index: 78.5% [CI: 70.50 86.51], P < 0.0001 in comparison with (a); (c) DRFs: C-index 85.5% [CI: 73.4, 99.6], P < 0.0001 in comparison with (b); and (d) DRF and GAP: C-index 91.0% [CI: 84.6, 97.2], P < 0.0001 in comparison with (c). Kaplan-Meier survival curves of patients stratified to low- and high-risk groups based on the DRFs showed a statistically significant (P < 0.0001) difference. The DRFs outperform the clinical predictors in predicting patient survival, and a combination of the DRFs and GAP index outperforms either one of these predictors. Our results indicate that the DRFs and their combination with clinical predictors provide an accurate prognostic biomarker for patients with RA-ILD.

Proceedings Article | 27 February 2018 Presentation + Paper

Detection of colorectal masses in CT colonography: application of deep residual networks for differentiating masses from normal colon anatomy

Janne Näppi, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 10575, 1057518 (2018) https://doi.org/10.1117/12.2293848

KEYWORDS: Colon, Network architectures, Computer aided diagnosis and therapy, Virtual colonoscopy, Colorectal cancer, Computer-aided diagnosis, Neural networks, Cancer, Computing systems, Data modeling

Read Abstract +

Proceedings Article | 10 March 2017 Paper

Enhancement of weakly tagged fecal materials in dual-energy CT colonography using spectral-driven iterative reconstruction technique

Radin Nasirudin, Janne Näppi, Toru Hironaka, Rie Tachibana, Hiroyuki Yoshida

Proceedings Volume 10132, 101323N (2017) https://doi.org/10.1117/12.2255673

KEYWORDS: Computed tomography, Image enhancement, Computer aided diagnosis and therapy, Virtual colonoscopy, Iodine, Computer aided design, CT reconstruction, Tissues, Visualization, Reconstruction algorithms

Read Abstract +

Dual-energy computed tomography is used increasingly in CT colonography (CTC). The combination of computer-aided detection (CAD) and dual-energy CTC has a high clinical value because it can automatically detect clinically significant colonic lesions in CTC images with higher accuracy than does single-energy CTC. While CAD has demonstrated its ability to detect small polyps, its performance is highly dependent on the quality of the input images. The presence of artifacts such as beam hardening and image noise in ultra-low-dose CTC may severely degrade detection performance for small polyps. A further limitation to the effectiveness of CAD are the weakly tagged fecal materials in the colon that may cause false-positive detections. In this work, we developed a dual-energy method for enhancing the appearance of weakly tagged fecal materials in CTC images. The proposed method consists of two stages: 1) the detection of weakly tagged fecal materials by use of sinogram-based image decomposition and 2) the enhancement of the detected tagged fecal materials in the images using an iterative reconstruction method. In the first stage, the ultra-low-dose dual-energy projection data obtained from a CT scanner are decomposed into two basis materials – soft tissue and fecal-tagged material (iodine). Virtual monochromatic projection data are calculated from the material decomposition at a pre-determined energy. The iodine-decomposed sinogram and the virtual monochromatic projection data are then used as input to an iterative reconstruction method. In the second stage, virtual monochromatic images are reconstructed iteratively while the intensity of weakly tagged iodine in the images is enhanced. The performance of the proposed method was assessed qualitatively and quantitatively. Preliminary results show that our method effectively enhances the visual appearance of weakly tagged fecal materials in the reconstructed CT images while reducing noise and improving the overall quality of the reconstructed images.

Proceedings Article | 3 March 2017 Paper

Electronic cleansing for CT colonography using spectral-driven iterative reconstruction

Radin Nasirudin, Janne Näppi, Toru Hironaka, Rie Tachibana, Hiroyuki Yoshida

Proceedings Volume 10134, 1013436 (2017) https://doi.org/10.1117/12.2255678

KEYWORDS: Image quality, Computer aided diagnosis and therapy, Colon, CT reconstruction, Virtual colonoscopy, Computed tomography, Computer-aided diagnosis, Reconstruction algorithms, X-ray computed tomography

Read Abstract +

Proceedings Article | 3 March 2017 Presentation + Paper

Deep learning of contrast-coated serrated polyps for computer-aided detection in CT colonography

Janne Näppi, Perry Pickhardt, David Kim, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 10134, 101340H (2017) https://doi.org/10.1117/12.2255634

KEYWORDS: Computer aided diagnosis and therapy, Computer-aided diagnosis, Virtual colonoscopy, Colon, Coating, Neural networks, Colorectal cancer, Feature extraction, Machine learning, Error control coding, Barium, Cancer, Convolutional neural networks

Read Abstract +

Proceedings Article | 3 March 2017 Presentation + Paper

Deep ensemble learning of virtual endoluminal views for polyp detection in CT colonography

Kensuke Umehara, Janne Näppi, Toru Hironaka, Daniele Regge, Takayuki Ishida, Hiroyuki Yoshida

Proceedings Volume 10134, 101340G (2017) https://doi.org/10.1117/12.2255606

KEYWORDS: Virtual colonoscopy, Neural networks, Colon, Computer-aided diagnosis, Machine learning, Cancer, Convolutional neural networks, Colorectal cancer, Algorithm development, 3D image processing, Detection and tracking algorithms

Read Abstract +

Proceedings Article | 3 March 2017 Presentation + Paper

Deep multi-spectral ensemble learning for electronic cleansing in dual-energy CT colonography

Rie Tachibana, Janne Näppi, Toru Hironaka, Se Hyung Kim, Hiroyuki Yoshida

Proceedings Volume 10134, 101340E (2017) https://doi.org/10.1117/12.2254726

KEYWORDS: Medical imaging, Image compression, Multispectral imaging, Image classification, RGB color model, Endoscopy, Virtual colonoscopy, Neural networks, Tissues, Image segmentation, Colon, Computed tomography

Read Abstract +

Proceedings Article | 24 March 2016 Paper

Performance evaluation of multi-material electronic cleansing for ultra-low-dose dual-energy CT colonography

Rie Tachibana, Naja Kohlhase, Janne Näppi, Toru Hironaka, Junko Ota, Takayuki Ishida, Daniele Regge, Hiroyuki Yoshida

Proceedings Volume 9785, 978526 (2016) https://doi.org/10.1117/12.2217140

KEYWORDS: Cancer, Colorectal cancer, Computed tomography, Iodine, Machine learning, Medical research

Read Abstract +

Proceedings Article | 24 March 2016 Paper

Deep learning for electronic cleansing in dual-energy CT colonography

Rie Tachibana, Janne Näppi, Toru Hironakaa, Se Hyung Kim, Hiroyuki Yoshida

Proceedings Volume 9785, 97851M (2016) https://doi.org/10.1117/12.2216446

KEYWORDS: Convolutional neural networks, Forward error correction, Tissues, Virtual colonoscopy, Medical imaging, Colon, Image compression, Iodine, Colorectal cancer, Natural surfaces

Read Abstract +

Proceedings Article | 24 March 2016 Paper

Deep transfer learning of virtual endoluminal views for the detection of polyps in CT colonography

Janne Näppi, Toru Hironaka, Daniele Regge, Hiroyuki Yoshida

Proceedings Volume 9785, 97852B (2016) https://doi.org/10.1117/12.2217260

KEYWORDS: Convolutional neural networks, Computer aided diagnosis and therapy, Virtual colonoscopy, Colon, Data modeling, Colorectal cancer, Feature extraction, Computing systems, Cancer

Read Abstract +

Proceedings Article | 20 March 2015 Paper

Context-specific method for detection of soft-tissue lesions in non-cathartic low-dose dual-energy CT colonography

Janne Näppi, Daniele Regge, Hiroyuki Yoshida

Proceedings Volume 9414, 94142Y (2015) https://doi.org/10.1117/12.2081284

KEYWORDS: Computer aided diagnosis and therapy, Colon, Virtual colonoscopy, Colorectal cancer, Tissues, Radiation effects, Visualization, Computed tomography, Cancer

Read Abstract +

Proceedings Article | 20 March 2015 Paper

Electronic cleansing for dual-energy CT colonography based on material decomposition and virtual monochromatic imaging

Rie Tachibana, Janne Näppi, Se Hyung Kim, Hiroyuki Yoshida

Proceedings Volume 9414, 94140Q (2015) https://doi.org/10.1117/12.2082375

KEYWORDS: Tissues, Virtual colonoscopy, Colon, Visualization, Forward error correction, Signal attenuation, Colorectal cancer, Iodine, Endoscopy, Machine learning

Read Abstract +

Proceedings Article | 18 March 2015 Paper

A comparison of material decomposition techniques for dual-energy CT colonography

Radin Nasirudin, Rie Tachibana, Janne Näppi, Kai Mei, Felix Kopp, Ernst Rummeny, Hiroyuki Yoshida, Peter Noël

Proceedings Volume 9412, 94124F (2015) https://doi.org/10.1117/12.2081982

KEYWORDS: Iodine, Computer aided diagnosis and therapy, Virtual colonoscopy, Computer aided design, Diagnostics, Computed tomography, Signal attenuation, Bone, Colon, Image segmentation

Read Abstract +

Proceedings Article | 20 March 2014 Paper

Progressive region-based colon extraction for computer-aided detection and quantitative imaging in cathartic and non-cathartic CT colonography

Janne Näppi, Yasuji Ryu, Hiroyuki Yoshida

Proceedings Volume 9035, 90352X (2014) https://doi.org/10.1117/12.2043890

KEYWORDS: Colon, Image segmentation, Interfaces, Computer aided diagnosis and therapy, Detection and tracking algorithms, Virtual colonoscopy, Algorithm development, Feature extraction, Lawrencium

Read Abstract +

Proceedings Article | 28 February 2013 Paper

Volumetric detection of flat lesions for minimal-preparation dual-energy CT colonography

Janne Näppi, Se Hyung Kim, Hiroyuki Yoshida

Proceedings Volume 8670, 86702E (2013) https://doi.org/10.1117/12.2008127

KEYWORDS: Target detection, Computer aided design, Computer aided diagnosis and therapy, Virtual colonoscopy, Iodine, Computing systems, Colon, 3D acquisition, Tomography

Read Abstract +

Proceedings Article | 23 February 2012 Paper

Automated detection of colorectal lesions with dual-energy CT colonography

Janne Näppi, Se Hyung Kim, Hiroyuki Yoshida

Proceedings Volume 8315, 83150Y (2012) https://doi.org/10.1117/12.911708

KEYWORDS: Computer aided diagnosis and therapy, Computed tomography, Virtual colonoscopy, Colon, Feature extraction, Computer aided design, Tissues, Iodine, Interfaces

Read Abstract +

Proceedings Article | 9 March 2011 Paper

Computer-aided detection of small bowel strictures in CT enterography

Nisha Sainani, Janne Näppi, Dushyant Sahani, Hiroyuki Yoshida

Proceedings Volume 7963, 79632J (2011) https://doi.org/10.1117/12.878232

KEYWORDS: Computer aided diagnosis and therapy, Computed tomography, Sensors, Computer aided design, Radiology, Diagnostics, 3D image processing, Image analysis, Image enhancement

Read Abstract +

Proceedings Article | 5 March 2011 Paper

Probabilistic method for context-sensitive detection of polyps in CT colonography

Janne Näppi, Daniele Regge, Hiroyuki Yoshida

Proceedings Volume 7963, 79631D (2011) https://doi.org/10.1117/12.878207

KEYWORDS: Computer aided diagnosis and therapy, Virtual colonoscopy, CAD systems, Computer aided design, Colon, Computed tomography, Computing systems, Solid modeling, Neural networks

Read Abstract +

Proceedings Article | 29 March 2007 Paper

Pseudo-enhancement correction for computer-aided detection in fecal-tagging CT colonography

Janne Näppi, Hiroyuki Yoshida, Michael Zalis, Wenli Cai, Philippe Lefere

Proceedings Volume 6514, 65140A (2007) https://doi.org/10.1117/12.710186

KEYWORDS: Colon, Signal attenuation, Computer aided design, Computed tomography, Visualization, Virtual colonoscopy, Computer aided diagnosis and therapy, CAD systems, Image segmentation

Read Abstract +

Proceedings Article | 29 March 2007 Paper

Delineation of tagged region by use of local iso-surface roughness in electronic cleansing for CT colonography

Wenli Cai, Hiroyuki Yoshida, Michael Zalis, Janne Näppi

Proceedings Volume 6514, 651409 (2007) https://doi.org/10.1117/12.710088

KEYWORDS: Image segmentation, Virtual colonoscopy, Computer aided design, Computed tomography, Colon, Computer aided diagnosis and therapy, Colorectal cancer, Image enhancement, Image classification

Read Abstract +

Proceedings Article | 16 March 2006 Paper

Centerline-based colon segmentation for CAD of CT colonography

Janne Näppi, Hans Frimmel, Hiroyuki Yoshida

Proceedings Volume 6144, 61445H (2006) https://doi.org/10.1117/12.653940

KEYWORDS: Colon, Computer aided diagnosis and therapy, Image segmentation, Computer aided design, Virtual colonoscopy, Computed tomography, Binary data, Rectum, Algorithm development

Read Abstract +

Proceedings Article | 10 March 2006 Paper

Digital bowel cleansing for computer-aided detection of polyps in fecal-tagging CT colonography

Wenli Cai, Janne Näppi, Micheal Zalis, Gordon Harris, Hiroyuki Yoshida

Proceedings Volume 6144, 614422 (2006) https://doi.org/10.1117/12.654168

KEYWORDS: Virtual colonoscopy, Colon, Image segmentation, Computer aided design, Computer aided diagnosis and therapy, Computed tomography, X-ray computed tomography, Magnesium, Integration

Read Abstract +

Proceedings Article | 14 April 2005 Paper

Ranking of polyp candidates for CAD in CT colonography

Janne Nappi, Hiroyuki Yoshida

Proceedings Volume 5746, (2005) https://doi.org/10.1117/12.596432

KEYWORDS: Computer aided design, Virtual colonoscopy, Computer aided diagnosis and therapy, Colon, Image segmentation, Computed tomography, Colorectal cancer, Feature extraction, Cancer

Read Abstract +

Proceedings Article | 12 May 2004 Paper

New high-performance CAD scheme for the detection of polyps in CT colonography

Janne Nappi, Hans Frimmel, Abraham Dachman, Hiroyuki Yoshida

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.536127

KEYWORDS: Image segmentation, Colon, Computer aided design, Computer aided diagnosis and therapy, Virtual colonoscopy, Neural networks, Colorectal cancer, Diagnostics, Computed tomography

Read Abstract +

Proceedings Article | 15 May 2003 Paper

Computer-aided detection of polyps and masses for CT colonography

Janne Naeppi, Hans Frimmel, Abraham Dachman, Hiroyuki Yoshida

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481380

KEYWORDS: Computer aided diagnosis and therapy, Colon, Virtual colonoscopy, Computer aided design, Image segmentation, Computed tomography, Colorectal cancer, Stomach, Diagnostics

Read Abstract +

Proceedings Article | 2 May 2003 Paper

Fast and robust method to compute colon centerline in CT colonography

Hans Frimmel, Janne Naeppi, Hiroyuki Yoshida

Proceedings Volume 5031, (2003) https://doi.org/10.1117/12.480666

KEYWORDS: Colon, Image segmentation, Virtual colonoscopy, Distance measurement, Computed tomography, Colorectal cancer, Endoscopy, Lung, Stomach, Optical inspection

Read Abstract +

Proceedings Article | 9 May 2002 Paper

Computer-aided diagnosis in CT colonography: detection of polyps based on geometric and texture features

Hiroyuki Yoshida, Janne Naeppi, Hans Frimmel, Abraham Dachman

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467082

KEYWORDS: Colon, Computer aided diagnosis and therapy, Image segmentation, Computer aided design, Virtual colonoscopy, Fuzzy logic, Computed tomography, Data analysis, Colorectal cancer, Statistical analysis

Read Abstract +

Proceedings Article | 24 April 2002 Paper

Effect of knowledge-guided colon segmentation in automated detection of polyps in CT colonography

Janne Nappi, Abraham Dachman, Peter MacEneaney, Hiroyuki Yoshida

Proceedings Volume 4683, (2002) https://doi.org/10.1117/12.463586

KEYWORDS: Image segmentation, Colon, Computer aided diagnosis and therapy, Virtual colonoscopy, Computer aided design, Rectum, Stomach, Lung, Computed tomography

Read Abstract +

Proceedings Article | 21 May 1999 Paper

Computerized diagnosis of breast calcifications using specimen radiography and simulated calcifications

Janne Naeppi, Peter Dean, Olli Nevalainen, Sakari Toikkanen

Proceedings Volume 3661, (1999) https://doi.org/10.1117/12.348533

KEYWORDS: Particles, Image segmentation, Mammography, Radiography, Breast, Image quality, Computer simulations, Computing systems, Imaging systems, Spatial resolution

Read Abstract +

Proceedings Article | 25 April 1997 Paper

Mammographic feature generator for evaluation of image analysis algorithms

Janne Naeppi, Peter Dean

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274180

KEYWORDS: Mammography, Image analysis, Modeling, Computing systems, Feature extraction, Visualization, Tissues, Digital mammography, Plasma, Image segmentation

Read Abstract +

Showing 5 of 56 publications

Conference Committee Involvement (21)

Computer-Aided Diagnosis

17 February 2025 | San Diego, California, United States

Computer-Aided Diagnosis

19 February 2024 | San Diego, California, United States

Computer-Aided Diagnosis

20 February 2023 | San Diego, California, United States

Computer-Aided Diagnosis

21 February 2022 | San Diego, California, United States

Computer-Aided Diagnosis

15 February 2021 | Online Only, California, United States

Computer-Aided Diagnosis

16 February 2020 | Houston, Texas, United States

Computer-Aided Diagnosis

17 February 2019 | San Diego, California, United States

Computer-Aided Diagnosis

12 February 2018 | Houston, Texas, United States

Computer-Aided Diagnosis

13 February 2017 | Orlando, Florida, United States

Computer-Aided Diagnosis Posters

13 February 2017 | Orlando, FL, United States

Computer-Aided Diagnosis

28 February 2016 | San Diego, California, United States

Computer-Aided Diagnosis

22 February 2015 | Orlando, Florida, United States

Computer-Aided Diagnosis

18 February 2014 | San Diego, California, United States

Computer-Aided Diagnosis

12 February 2013 | Lake Buena Vista (Orlando Area), Florida, United States

Computer-Aided Diagnosis

7 February 2012 | San Diego, California, United States

Computer-Aided Diagnosis

15 February 2011 | Lake Buena Vista (Orlando), Florida, United States

International Workshop on Machine Learning in Medical Imaging

20 September 2010 |

Challenges and Opportunities in Virtual Colonoscopy and Abdominal Imaging

20 September 2010 |

Computer-Aided Diagnosis

16 February 2010 | San Diego, California, United States

Computational and Visualization Challenges in the New Era of Virtual Colonoscopy

6 September 2008 |

Computational and Visualization Challenges in the New Era of Virtual Colonoscopy

6 September 2008 |

Showing 5 of 21 Conference Committees

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years