Mr. Futa Goshima Profile

Futa Goshima

at Kanazawa Univ

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Publications (3)

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Proceedings Article | 1 April 2024 Poster + Paper

Deep learning-based algorithm to segment pediatric and adult lungs from dynamic chest radiography images using virtual patient datasets

Futa Goshima, Rie Tanaka, Isao Matsumoto, Noriyuki Ohkura, Takatoshi Abe, William Paul Segars, Ehsan Abadi, Ehsan Samei

Proceedings Volume 12925, 1292541 (2024) https://doi.org/10.1117/12.3006963

KEYWORDS: Image segmentation, Lung, Education and training, Data modeling, Chest, Radiography, Motion models, Mixtures, Matrices, X-ray imaging

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Dynamic chest radiography (DCR) enables the evaluation of lung function based on changes in lung density, lung area, and diaphragm level due to respiration. The need for lung segmentation techniques for sequential chest images is growing. Thus, this study was aimed at developing a deep learning–based lung segmentation technique for DCR across all age groups using virtual patient images. DCR images of 53 patients (M:F = 34:19, age: 1–88 years, median age: 63 years) were used. Owing to the difficulty in collecting a large dataset of pediatric DCR images, the 4D extended cardiac-torso phantom (XCAT phantom) was used to augment the pediatric data. A total of ten pediatric XCAT phantoms (five males and females each, age: 0–15 years) of virtual patients were generated and projected. Two deep-learning models, U-net and DeepLabv3 using MobileNetv3 as the backbone, were implemented. They were trained to estimate the lung segmentation masks using DCR image datasets consisting of only real or a mixture of real and virtual patients. Dice similarity coefficient (DSC) and intersection over union (IoU) were used as evaluation metrics. When trained only on real patients, for both the metrics, DeepLabv3 (DSC/IoU: 0.902/0.822) exhibited higher values than U-net (DSC/IoU: 0.791/0.673). When trained on dataset of a mixture of real and virtual patients, values of both the metrics improved in both models (DSC/IoU: 0.906/0.828 and 0.795/0.677 for DeepLabv3 and U-net, respectively). These results indicate that the developed model, that is, the combination of DeepLabv3 and XCAT-based augmentation methods, is effective for the lung segmentation of DCR images of various respiratory phases for all age groups.

Proceedings Article | 7 April 2023 Poster + Paper

Bone suppression technique for multidirectional dynamic chest radiography: a virtual imaging trial

Futa Goshima, Rie Tanaka, William Paul Segars, Ehsan Abadi, Ehsan Samei

Proceedings Volume 12463, 124634S (2023) https://doi.org/10.1117/12.2654119

KEYWORDS: Bone, Chest, Biological imaging, Shadows, Radiography, Tumors, Education and training, Lung, Video, Motion models

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Prediction of pleural invasion in lung cancer is crucial in planning appropriate operating procedures and can be assessed using dynamic chest radiography (DCR). However, this assessment is negatively affected by rib shadows in conventional images. The purpose of this study was to develop a deep learning-based bone suppression technique for DCR in various projection directions. Twenty breathing XCAT phantoms with lung tumors and a pair of phantoms consisting only of bone structures were generated. The XCAT phantoms were virtually projected from six directions, resulting in 54000 multidirectional chest X-ray images and were used for training a pix2pix model to estimate bone images from original images. Bone suppression (BS) images were created by subtracting the bone images from the original images and then evaluated based on the peak signal to noise ratio (PSNR), structural similarity index measure (SSIM), and Fréchet Video Distance (FVD). Clinical cases were processed using the trained model as well. Furthermore, lung tumors on the original and BS images were tracked using OpenCV template matching, and the tracking accuracy was compared. The PSNR, SSIM, and FVD were 0.9966, 52.20 and 136.9, respectively. In the visual evaluation, we confirmed the effect of BS without temporal fluctuation of pixel values in both the resulting images of the XCAT phantom and real patients. Furthermore, precise tracking of the targeted tumor was achieved on the resulting BS images even in oblique directions, without any interruption from the rib shadows. These results indicate that our proposed method can effectively reduce bone shadows as well as temporal variations in the effect of bone suppression.

SPIE Journal Paper | 18 January 2022

Deep learning versus the human visual system for detecting motion blur in radiography

Rie Tanaka, Shiho Nozaki, Futa Goshima, Junji Shiraishi

JMI, Vol. 9, Issue 01, 015501, (January 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.1.015501

KEYWORDS: LCDs, Motion detection, Radiography, Diagnostics, Visual system, Medical imaging, Digital imaging, Image resolution, Software development, Convolution

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Purpose: The necessity of image retakes is initially determined on a preview monitor equipped with an operating system; therefore, some image blurring is only noticed later, on a high-resolution monitor. The purpose of this study is to investigate blur detection performance on radiographs via a deep learning approach compared with human observers. Approach: A total of 99 radiographs (blurry 57, nonblurry 42) were independently observed and rated by six observers using preview and diagnostic liquid crystal displays (LCDs). The deep convolution neural network (DCNN) was trained and tested using ninefold cross-validation. The average areas under the ROC curves (AUCs) were calculated for each observer with LCDs and by stand-alone DCNN for each test session and then statistically tested using a 95% confidence interval. Results: The average AUCs were 0.955 for stand-alone DCNN and 0.827 and 0.947 for human observers using preview and diagnostic LCDs, respectively. The DCNN revealed a high performance for image motion blur on digital radiographs (sensitivity 94.8%, specificity 96.8%, and accuracy 95.6%), along with the capability to detect a slight motion blur that was overlooked by human observers with a preview LCD. There were no cases of motion blur overlooked by the stand-alone DCNN, of which some were incorrectly recognized as nonblurry by human observers. Conclusions: The deep learning-based approach was capable of distinguishing slight motion blur that was unnoticeable on a preview LCD, and thus, is expected to aid the human visual system for detecting blurred images in the initial review of digital radiographs.

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