Mohsen Heidari - IEEE Xplore Author Profile
Subscribe
ADVANCED SEARCH

Author details

< Back

Mohsen Heidari

Publications
25
Citations
59
Publications by Year
20152025
Co-Authors:
Show All Co-Authors (13)
Headshot silhouette indicating that no photo is available for this author

Mohsen Heidari

Affiliation

Department of Computer Science
Indiana University
Bloomington, IN, USA

Publication Topics

Biography

Mohsen Heidari received the B.Sc. and M.Sc. degrees in electrical engineering from the Sharif University of Technology in 2011 and 2013, respectively, and the M.Sc. degree in mathematics and the Ph.D. degree in electrical engineering from the University of Michigan, Ann Arbor, MI, USA, in 2017 and 2019, respectively. From 2019 to 2021, he was a Post-Doctoral Research Associate with the NSF Center for Science of Information and the Department of Computer Science, Purdue University. From 2021 to 2022, he was a Visiting Assistant Professor with the Department of Computer Science, Purdue University. He is currently an Assistant Professor with the Department of Computer Science, Indiana University, Bloomington, IN, USA. His research interests in...
Publications
25
Citations
59
Publications by Year
20152025
Co-Authors:
Show All Co-Authors (13)
Author's Published Works

Showing 1-23 of 23 results

  • Sort

Filter Results

Show

Sort By

Results

Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound

Gautam Gare;Hai Tran;Bennett DeBoisblanc;Ricardo Rodriguez;John Galeotti

2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)
Year: 2025 | Conference Paper |
With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analysis. Several AI-based patient severity scoring models have been proposed that rely on scoring the appearance of the ultrasound scans. AI models are tra...Show More

Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound

Gautam Gare;Hai Tran;Bennett DeBoisblanc;Ricardo Rodriguez;John Galeotti

Year: 2025 | Conference Paper |

Multi-Phase Attention for Early Stage Hepatocellular Carcinoma Segmentation from Contrast Enhanced Liver MRI

Nishanth Arun;Alexander Weston;Candice Bolan;Beau Toskich;Fernando Gil Lopez;John Galeotti

2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)
Year: 2025 | Conference Paper |
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally, primarily due to late-stage detection. Accurate segmentation of HCC from multiphase MRI scans plays a critical role in early detection and treatment planning, but manual segmentation is labor-intensive owing to the heterogenous and diffuse nature of these tumors. In this study, we propose a novel deep learning fra...Show More

Multi-Phase Attention for Early Stage Hepatocellular Carcinoma Segmentation from Contrast Enhanced Liver MRI

Nishanth Arun;Alexander Weston;Candice Bolan;Beau Toskich;Fernando Gil Lopez;John Galeotti

Year: 2025 | Conference Paper |

Motion-Aware Needle Segmentation in Ultrasound Images

Raghavv Goel;Cecilia Morales;Manpreet Singh;Artur Dubrawski;John Galeotti;Howie Choset

2024 IEEE International Symposium on Biomedical Imaging (ISBI)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Segmenting a moving needle in ultrasound images is challenging due to the presence of artifacts, noise, and needle occlusion. This task becomes even more demanding in scenarios where data availability is limited. In this paper, we present a novel approach for needle segmentation for 2D ultrasound that combines classical Kalman Filter (KF) techniques with data-driven learning, incorporating both ne...Show More

Motion-Aware Needle Segmentation in Ultrasound Images

Raghavv Goel;Cecilia Morales;Manpreet Singh;Artur Dubrawski;John Galeotti;Howie Choset

Year: 2024 | Conference Paper |

Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation

FNU Abhimanyu;Andrew L. Orekhov;Ananya Bal;John Galeotti;Howie Choset

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
This paper presents a deep-learning model for deformable registration of ultrasound images at online rates, which we call U-RAFT. As its name suggests, U-RAFT is based on RAFT, a convolutional neural network for estimating optical flow. U-RAFT, however, can be trained in an unsupervised manner and can generate synthetic images for training vessel segmentation models. We propose and compare the reg...Show More

Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation

FNU Abhimanyu;Andrew L. Orekhov;Ananya Bal;John Galeotti;Howie Choset

Year: 2023 | Conference Paper |

A Curvature and Trajectory Optimization-based 3D Surface Reconstruction Pipeline for Ultrasound Trajectory Generation

Ananya Bal;Ashutosh Gupta;Fnu Abhimanyu;John Galeotti;Howie Choset

2023 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2023 | Conference Paper |
Ultrasound scanning is an efficient imaging modality preferred for quick medical procedures. However, due to the lack of skilled sonographers, researchers have developed many Robotic Ultrasound System (RUS) prototypes for various procedures. Most of these systems have a human-in-the-loop and require an expert to point the robot to the region of the subject to be scanned. Only a few systems try to ...Show More

A Curvature and Trajectory Optimization-based 3D Surface Reconstruction Pipeline for Ultrasound Trajectory Generation

Ananya Bal;Ashutosh Gupta;Fnu Abhimanyu;John Galeotti;Howie Choset

Year: 2023 | Conference Paper |

A Comparison of Point Cloud Registration Techniques for on-site Disaster Data from the Surfside Structural Collapse

Ananya Bal;Robert Ladig;Pranav Goyal;John Galeotti;Howie Choset;David Merrick;Robin Murphy

2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
3D representations of geographical surfaces in the form of dense point clouds can be a valuable tool for documenting and reconstructing a structural collapse, such as the 2021 Champlain Towers Condominium collapse in Surfside, Florida. Point cloud data reconstructed from aerial footage taken by uncrewed aerial systems at frequent intervals from a dynamic search and rescue scene poses significant c...Show More

A Comparison of Point Cloud Registration Techniques for on-site Disaster Data from the Surfside Structural Collapse

Ananya Bal;Robert Ladig;Pranav Goyal;John Galeotti;Howie Choset;David Merrick;Robin Murphy

Year: 2022 | Conference Paper |

Autonomous Ultrasound Scanning using Bayesian Optimization and Hybrid Force Control

Raghavv Goel;Fnu Abhimanyu;Kirtan Patel;John Galeotti;Howie Choset

2022 International Conference on Robotics and Automation (ICRA)
Year: 2022 | Conference Paper |
Cited by: Papers (20)
Ultrasound scanning is an imaging technique that aids medical professionals in diagnostics and interventional procedures. However, a trained human-in-the-loop (HITL) with a radiologist is required to perform the scanning procedure. We seek to create a novel ultrasound system that can provide imaging in the absence of a trained radiologist, say for patients in the field who suffered injuries after ...Show More

Autonomous Ultrasound Scanning using Bayesian Optimization and Hybrid Force Control

Raghavv Goel;Fnu Abhimanyu;Kirtan Patel;John Galeotti;Howie Choset

Year: 2022 | Conference Paper |

Toward Robotically Automated Femoral Vascular Access

Nico Zevallos;Evan Harber;Abhimanyu;Kirtan Patel;Yizhu Gu;Kenny Sladick;Francis Guyette;Leonard Weiss;Michael R. Pinsky;Hernando Gomez;John Galeotti;Howie Choset

2021 International Symposium on Medical Robotics (ISMR)
Year: 2021 | Conference Paper |
Cited by: Papers (7)
Advanced resuscitative technologies, such as Extra Corporeal Membrane Oxygenation (ECMO) cannulation or Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA), are technically difficult even for skilled medical personnel. This paper describes the core technologies that comprise a teleoperated system capable of granting femoral vascular access, an essential step in these procedures, and ...Show More

Toward Robotically Automated Femoral Vascular Access

Nico Zevallos;Evan Harber;Abhimanyu;Kirtan Patel;Yizhu Gu;Kenny Sladick;Francis Guyette;Leonard Weiss;Michael R. Pinsky;Hernando Gomez;John Galeotti;Howie Choset

Year: 2021 | Conference Paper |

Robust Skin-Feature Tracking in Free-Hand Video from Smartphone or Robot-Held Camera, to Enable Clinical-Tool Localization and Guidance

Chun-Yin Huang;John Galeotti

2021 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Our novel skin-feature visual-tracking algorithm enables anatomic vSLAM and (by extension) localization of clinical tools relative to the patient’s body. Tracking naturally occurring features is challenging due to patient uniqueness, deformability, and lack of an accurate a-priori 3D geometric model. Our method (i) tracks skin features in a smartphone-camera video sequence, (ii) performs anatomic ...Show More

Robust Skin-Feature Tracking in Free-Hand Video from Smartphone or Robot-Held Camera, to Enable Clinical-Tool Localization and Guidance

Chun-Yin Huang;John Galeotti

Year: 2021 | Conference Paper |

Uncertainty-Based Adaptive Data Augmentation For Ultrasound Imaging Anatomical Variations

Edward Chen;Howie Choset;John Galeotti

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
Data augmentation remains to be a simple and inexpensive method for generalizing across unseen domains. Current data augmentation methods for ultrasound imaging involve simple image transformations - rotations, flips, skews, and blurs - but are not able to adapt to the current state of the deep learning model. We present the first online adaptive data augmentation method that is able to generate s...Show More

Uncertainty-Based Adaptive Data Augmentation For Ultrasound Imaging Anatomical Variations

Edward Chen;Howie Choset;John Galeotti

Year: 2021 | Conference Paper |

Dense Pixel-Labeling For Reverse-Transfer And Diagnostic Learning On Lung Ultrasound For Covid-19 And Pneumonia Detection

Gautam Rajendrakumar Gare;Andrew Schoenling;Vipin Philip;Hai V Tran;Bennett P deBoisblanc;Ricardo Luis Rodriguez;John Michael Galeotti

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Year: 2021 | Conference Paper |
Cited by: Papers (9)
We propose using a pre-trained segmentation model to perform diagnostic classification in order to achieve better generalization and interpretability, terming the technique reverse-transfer learning. We present an architecture to convert segmentation models to classification models. We compare and contrast dense vs sparse segmentation labeling and study its impact on diagnostic classification. We ...Show More

Dense Pixel-Labeling For Reverse-Transfer And Diagnostic Learning On Lung Ultrasound For Covid-19 And Pneumonia Detection

Gautam Rajendrakumar Gare;Andrew Schoenling;Vipin Philip;Hai V Tran;Bennett P deBoisblanc;Ricardo Luis Rodriguez;John Michael Galeotti

Year: 2021 | Conference Paper |

Ultrasound-Based Tracking Of Partially In-Plane, Curved Needles

Wanwen Chen;Kathan Nilesh Mehta;Bhumi Dinesh Bhanushali;John Galeotti

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
We present a novel algorithm for needle tracking in ultrasound-guided needle insertion. Most previous research assumes that in ultrasound images the needle is a straight and bright line, but needles can bend due to the interaction with heterogeneous tissue. We utilize a novel weighted RANSAC curve fitting method combined with probabilistic Hough transform to track the curved needle robustly, and t...Show More

Ultrasound-Based Tracking Of Partially In-Plane, Curved Needles

Wanwen Chen;Kathan Nilesh Mehta;Bhumi Dinesh Bhanushali;John Galeotti

Year: 2021 | Conference Paper |

Ultrasound Confidence Maps Of Intensity And Structure Based On Directed Acyclic Graphs And Artifact Models

Alex Ling Yu Hung;Wanwen Chen;John Galeotti

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
Ultrasound imaging has been improving, but continues to suffer from inherent artifacts that are challenging to model, such as attenuation, shadowing, diffraction, speckle, etc. These artifacts can potentially confuse image analysis algorithms unless an attempt is made to assess the certainty of individual pixel values. Our novel confidence algorithms analyze pixel values using a directed acyclic g...Show More

Ultrasound Confidence Maps Of Intensity And Structure Based On Directed Acyclic Graphs And Artifact Models

Alex Ling Yu Hung;Wanwen Chen;John Galeotti

Year: 2021 | Conference Paper |

Learning to Segment Vessels from Poorly Illuminated Fundus Images

Vibha Nasery;Krishna Bairavi Soundararajan;John Galeotti

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
Year: 2020 | Conference Paper |
Cited by: Papers (6)
Segmentation of retinal vessels is important for determining various disease conditions, but deep learning approaches have been limited by the unavailability of large, publicly available, and annotated datasets. The paper addresses this problem and analyses the performance of U-Net architecture on DRIVE and RIM-ONE datasets. A different approach for data augmentation using vignetting masks is pres...Show More

Learning to Segment Vessels from Poorly Illuminated Fundus Images

Vibha Nasery;Krishna Bairavi Soundararajan;John Galeotti

Year: 2020 | Conference Paper |

Learning To Segment Corneal Tissue Interfaces In Oct Images

Tejas Sudharshan Mathai;Kira L Lathrop;John Galeotti

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
Year: 2019 | Conference Paper |
Cited by: Papers (8)
Accurate and repeatable delineation of corneal tissue interfaces is necessary for surgical planning during anterior segment interventions, such as Keratoplasty. Designing an approach to identify interfaces, which generalizes to datasets acquired from different Optical Coherence Tomographic (OCT) scanners, is paramount. In this paper, we present a Convolutional Neural Network (CNN) based framework ...Show More

Learning To Segment Corneal Tissue Interfaces In Oct Images

Tejas Sudharshan Mathai;Kira L Lathrop;John Galeotti

Year: 2019 | Conference Paper |

Visualizing the palisades of Vogt: Limbal registration by surface segmentation

Tejas Sudharshan Mathai;John Galeotti;Kira L Lathrop

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
In this paper, a novel fully-automated algorithm is presented that enables the visualization of the Palisades of Vogt (POV) imaged using Optical Coherence Tomography (OCT). The algorithm segments the anterior limbal surface in each B-scan of a volume, uses it to guide the registration of individual B-scans to a reference B-scan, and creates an aligned 3D volume of the imaged limbal region. Accurat...Show More

Visualizing the palisades of Vogt: Limbal registration by surface segmentation

Tejas Sudharshan Mathai;John Galeotti;Kira L Lathrop

Year: 2018 | Conference Paper |

Ultrasound Tracking Using ProbeSight: Camera Pose Estimation Relative to External Anatomy by Inverse Rendering of a Prior High-Resolution 3D Surface Map

Jihang Wang;Chengqian Che;John Galeotti;Samantha Horvath;Vijay Gorantla;George Stetten

2017 IEEE Winter Conference on Applications of Computer Vision (WACV)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
This paper addresses the problem offreehand ultrasound probe tracking without requiring an external tracking device, by mounting a video camera on the probe to identify location relative to the patient's external anatomy. By pre-acquiring a high-resolution 3D surface map as an atlas ofthe anatomy, we eliminate the needfor artificial skin markers. We use an OpenDR pipeline for inverse rendering and...Show More

Ultrasound Tracking Using ProbeSight: Camera Pose Estimation Relative to External Anatomy by Inverse Rendering of a Prior High-Resolution 3D Surface Map

Jihang Wang;Chengqian Che;John Galeotti;Samantha Horvath;Vijay Gorantla;George Stetten

Year: 2017 | Conference Paper |

FingerSight: Fingertip Haptic Sensing of the Visual Environment

Samantha Horvath;John Galeotti;Bing Wu;Roberta Klatzky;Mel Siegel;George Stetten

IEEE Journal of Translational Engineering in Health and Medicine
Year: 2014 | Volume: 2 | Journal Article |
We present a novel device mounted on the fingertip for acquiring and transmitting visual information through haptic channels. In contrast to previous systems in which the user interrogates an intermediate representation of visual information, such as a tactile display representing a camera generated image, our device uses a fingertip-mounted camera and haptic stimulator to allow the user to feel v...Show More

FingerSight: Fingertip Haptic Sensing of the Visual Environment

Samantha Horvath;John Galeotti;Bing Wu;Roberta Klatzky;Mel Siegel;George Stetten

Year: 2014 | Volume: 2 | Journal Article |

Descending Variance Graphs for Segmenting Neurological Structures

George Stetten;Cindy Wong;Vikas Shivaprabhu;Ada Zhang;Samantha Horvath;Jihang Wang;John Galeotti;Vijay Gorantla;Howard Aizenstein

2013 International Workshop on Pattern Recognition in Neuroimaging
Year: 2013 | Conference Paper |
Cited by: Papers (2)
We present a novel and relatively simple method for clustering pixels into homogeneous patches using a directed graph of edges between neighboring pixels. For a 2D image, the mean and variance of image intensity is computed within a circular region centered at each pixel. Each pixel stores its circle's mean and variance, and forms the node in a graph, with possible edges to its 4 immediate neighbo...Show More

Descending Variance Graphs for Segmenting Neurological Structures

George Stetten;Cindy Wong;Vikas Shivaprabhu;Ada Zhang;Samantha Horvath;Jihang Wang;John Galeotti;Vijay Gorantla;Howard Aizenstein

Year: 2013 | Conference Paper |

Fingersight: Fingertip Visual Haptic Sensing and Control

George Stetten;Roberta Klatzky;Brock Nichol;John Galeotti;Kenneth Rockot;Kimberly Zawrotny;David Weiser;Nathan Sendgikoski;Samantha Horvath

2007 IEEE International Workshop on Haptic, Audio and Visual Environments and Games
Year: 2007 | Conference Paper |
Cited by: Papers (4)
We present a new method of sensing the 3D visual environment and controlling objects within it. The human hand is well suited to interrogate and manipulate objects by physical contact; however, the hand is limited to surfaces within its reach. Extending the hand's innate ability, we mount miniature cameras on individual fingertips, permitting rapid sweeping through the 3D visual environment at gre...Show More

Fingersight: Fingertip Visual Haptic Sensing and Control

George Stetten;Roberta Klatzky;Brock Nichol;John Galeotti;Kenneth Rockot;Kimberly Zawrotny;David Weiser;Nathan Sendgikoski;Samantha Horvath

Year: 2007 | Conference Paper |

AUTOMATED SEGMENTATION OF THE RIGHT HEART USING AN OPTIMIZED SHELLS AND SPHERES ALGORITHM

G. Stetten;M. Sacks;A. Powell;J.E. Mayer;D. Gottlieb;R. Tamburo;J. Galeotti;K. Rockot;C.A. Cow;G. Stetten;M. Sacks;A. Powell;J.E. Mayer;D. Gottlieb;R. Tamburo;J. Galeotti;K. Rockot;C.A. Cow

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Year: 2007 | Conference Paper |
Cited by: Papers (3)
We have developed a novel framework for medical image analysis, known as shells and spheres. This framework utilizes spherical operators of variable radius centered at each image pixel and sized to reach, but not cross, the nearest object boundary. Statistical population tests are performed on adjacent spheres to compare image regions across boundaries. Previously, our framework was applied to seg...Show More

AUTOMATED SEGMENTATION OF THE RIGHT HEART USING AN OPTIMIZED SHELLS AND SPHERES ALGORITHM

G. Stetten;M. Sacks;A. Powell;J.E. Mayer;D. Gottlieb;R. Tamburo;J. Galeotti;K. Rockot;C.A. Cow;G. Stetten;M. Sacks;A. Powell;J.E. Mayer;D. Gottlieb;R. Tamburo;J. Galeotti;K. Rockot;C.A. Cow

Year: 2007 | Conference Paper |

LOCALIZING AMYGDALA STRUCTURE DIFFERENCES IN LATE-LIFE DEPRESSION

Robert J. Tamburo;Greg J. Siegle;George D. Stetten;C. Aaron Cois;Ken J. Rockot;John M. Galeotti;Charles F. Reynolds;Howard J. Aizenstein

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Year: 2007 | Conference Paper |
The amygdala is critical for processing emotional information and plays an important role in late-life depression (LLD). Volumetric studies of the amygdala have been inconclusive with reports of increased, decreased, and no volume changes. This study investigates amygdala shape morphometry to test the hypothesis that if structural changes are specific to certain nuclei, then shape changes may be a...Show More

LOCALIZING AMYGDALA STRUCTURE DIFFERENCES IN LATE-LIFE DEPRESSION

Robert J. Tamburo;Greg J. Siegle;George D. Stetten;C. Aaron Cois;Ken J. Rockot;John M. Galeotti;Charles F. Reynolds;Howard J. Aizenstein

Year: 2007 | Conference Paper |

Enhanced snake based segmentation of vocal folds

S. Allin;J. Galeotti;G. Stetten;S.H. Dailey

2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821)
Year: 2004 | Conference Paper |
Cited by: Papers (16)
We present a system to segment the medial edges of the vocal folds from stroboscopic video. The system has two components. The first learns a color transformation that optimally discriminates, according to the Fisher linear criterion, between the trachea and vocal folds. Using this transformation, it is able to make a coarse segmentation of vocal fold boundaries. The second component uses an activ...Show More

Enhanced snake based segmentation of vocal folds

S. Allin;J. Galeotti;G. Stetten;S.H. Dailey

Year: 2004 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.