Shota Ichikawa

Assistant Professor

Niigata University

Japan

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Shota Ichikawa is an Assistant Professor at Niigata University, Japan. His research areas encompass advanced imaging techniques in medical diagnostics, particularly focusing on deep learning applications in computed tomography and magnetic resonance imaging. Recent articles highlight his work on algorithm development for predicting cancer recurrence, improving cerebrovascular imaging, and enhancing mammography through machine learning methodologies.

Shota Ichikawa

Research Interests

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Articles (20)

Time series-derived fractal dimension of CT perfusion in acute ischemic stroke: a promising marker for hypoperfused tissue quantification

Recurrence prediction of invasive ductal carcinoma from preoperative contrast-enhanced computed tomography using deep convolutional neural network

Radiomic Fingerprints: Automated Personal Identification in Mass Disasters Using Shape-Based Features of Thoracic Vertebral Bodies on CT

Improving Cerebrovascular Imaging with Deep Learning: Semantic Segmentation for Time-of-Flight Magnetic Resonance Angiography Maximum Intensity Projection Image Enhancement

The Effectiveness of Semi-Supervised Learning Techniques in Identifying Calcifications in X-ray Mammography and the Impact of Different Classification Probabilities

Development of a Method for Estimating the Angle of Lumbar Spine X-ray Images Using Deep Learning with Pseudo X-ray Images Generated from Computed Tomography

Deep learning-based correction for time truncation in cerebral computed tomography perfusion.

Automatic Aortic Valve Extraction Using Deep Learning with Contrast-Enhanced Cardiac CT Images

Machine learning‐based estimation of patient body weight from radiation dose metrics in computed tomography

Deep learning‐based body weight from scout images can be an alternative to actual body weight in CT radiation dose management

Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN

Acquisition time reduction in pediatric 99mTc‐DMSA planar imaging using deep learning

Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques

Prediction of body weight from chest radiographs using deep learning with a convolutional neural network

Development of a Mammography Calcification Detection Algorithm Using Deep Learning with Resolution-Preserved Image Patch Division

Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithm

A deep-learning method using computed tomography scout images for estimating patient body weight

Comparison of a Bayesian estimation algorithm and singular value decomposition algorithms for 80-detector row CT perfusion in patients with acute ischemic stroke

Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke

CT dose management of adult patients with unknown body weight using an effective diameter

Collaborators (5)

Kohsuke Kudo

Yohan Kondo

Naoya Takahashi

Satoshi YOKOYAMA

Hiroyuki Sugimori

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