no code implementations • 11 Feb 2025 • Krithika Iyer, Mokshagna Sai Teja Karanam, Shireen Elhabian
However, the effectiveness of SSM in anatomy evaluation hinges on the quality and robustness of the shape models.
no code implementations • 4 Feb 2025 • Mokshagna Sai Teja Karanam, Krithika Iyer, Sarang Joshi, Shireen Elhabian
The framework offers two main contributions: (1) a data-driven regularization strategy that incorporates population-level anatomical statistics to enhance transformation validity and (2) a linearized latent space that enables compact and interpretable deformation fields for efficient population morphometrics analysis.
no code implementations • 20 Dec 2024 • Krithika Iyer, Shireen Elhabian, Sarang Joshi
Image registration is a core task in computational anatomy that establishes correspondences between images.
no code implementations • Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 • K M Arefeen Sultan, Md Hasibul Husain Hisham, Benjamin Orkild, Alan Morris, Eugene Kholmovski, Erik Bieging, Eugene Kwan, Ravi Ranjan, Ed DiBella, Shireen Elhabian
The pursuit of automated LGE MRI quality assessment is critical for enhancing diagnostic accuracy, standardizing evaluations, and improving patient outcomes.
1 code implementation • 15 May 2024 • Jadie Adams, Krithika Iyer, Shireen Elhabian
To address these challenges, we introduce a weakly supervised deep learning approach to predict SSM from images using point cloud supervision.
no code implementations • 15 May 2024 • Jadie Adams, Shireen Elhabian
Correspondence-based statistical shape modeling (SSM) stands as a powerful technology for morphometric analysis in clinical research.
no code implementations • 13 Oct 2023 • K M Arefeen Sultan, Benjamin Orkild, Alan Morris, Eugene Kholmovski, Erik Bieging, Eugene Kwan, Ravi Ranjan, Ed DiBella, Shireen Elhabian
To address this, we propose a two-stage deep-learning approach for automated LGE-MRI image diagnostic quality assessment.
no code implementations • 2 Oct 2023 • Abu Zahid Bin Aziz, Jadie Adams, Shireen Elhabian
The training is performed in multiple scales, and each scale utilizes the output from the previous scale.
1 code implementation • 30 Aug 2023 • Jianning Li, Zongwei Zhou, Jiancheng Yang, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Chongyu Qu, Tiezheng Zhang, Xiaoxi Chen, Wenxuan Li, Marek Wodzinski, Paul Friedrich, Kangxian Xie, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, Gian Marco Melito, Viet Duc Vu, Afaque R. Memon, Christopher Schlachta, Sandrine de Ribaupierre, Rajnikant Patel, Roy Eagleson, Xiaojun Chen, Heinrich Mächler, Jan Stefan Kirschke, Ezequiel de la Rosa, Patrick Ferdinand Christ, Hongwei Bran Li, David G. Ellis, Michele R. Aizenberg, Sergios Gatidis, Thomas Küstner, Nadya Shusharina, Nicholas Heller, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Anjany Sekuboyina, Maximilian Löffler, Hans Liebl, Reuben Dorent, Tom Vercauteren, Jonathan Shapey, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Achraf Ben-Hamadou, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Federico Bolelli, Costantino Grana, Luca Lumetti, Hamidreza Salehi, Jun Ma, Yao Zhang, Ramtin Gharleghi, Susann Beier, Arcot Sowmya, Eduardo A. Garza-Villarreal, Thania Balducci, Diego Angeles-Valdez, Roberto Souza, Leticia Rittner, Richard Frayne, Yuanfeng Ji, Vincenzo Ferrari, Soumick Chatterjee, Florian Dubost, Stefanie Schreiber, Hendrik Mattern, Oliver Speck, Daniel Haehn, Christoph John, Andreas Nürnberger, João Pedrosa, Carlos Ferreira, Guilherme Aresta, António Cunha, Aurélio Campilho, Yannick Suter, Jose Garcia, Alain Lalande, Vicky Vandenbossche, Aline Van Oevelen, Kate Duquesne, Hamza Mekhzoum, Jef Vandemeulebroucke, Emmanuel Audenaert, Claudia Krebs, Timo Van Leeuwen, Evie Vereecke, Hauke Heidemeyer, Rainer Röhrig, Frank Hölzle, Vahid Badeli, Kathrin Krieger, Matthias Gunzer, Jianxu Chen, Timo van Meegdenburg, Amin Dada, Miriam Balzer, Jana Fragemann, Frederic Jonske, Moritz Rempe, Stanislav Malorodov, Fin H. Bahnsen, Constantin Seibold, Alexander Jaus, Zdravko Marinov, Paul F. Jaeger, Rainer Stiefelhagen, Ana Sofia Santos, Mariana Lindo, André Ferreira, Victor Alves, Michael Kamp, Amr Abourayya, Felix Nensa, Fabian Hörst, Alexander Brehmer, Lukas Heine, Yannik Hanusrichter, Martin Weßling, Marcel Dudda, Lars E. Podleska, Matthias A. Fink, Julius Keyl, Konstantinos Tserpes, Moon-Sung Kim, Shireen Elhabian, Hans Lamecker, Dženan Zukić, Beatriz Paniagua, Christian Wachinger, Martin Urschler, Luc Duong, Jakob Wasserthal, Peter F. Hoyer, Oliver Basu, Thomas Maal, Max J. H. Witjes, Gregor Schiele, Ti-chiun Chang, Seyed-Ahmad Ahmadi, Ping Luo, Bjoern Menze, Mauricio Reyes, Thomas M. Deserno, Christos Davatzikos, Behrus Puladi, Pascal Fua, Alan L. Yuille, Jens Kleesiek, Jan Egger
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
1 code implementation • 6 Jul 2023 • Tushar Kataria, Beatrice Knudsen, Shireen Elhabian
In this study, we compare the performance of gland and cell segmentation tasks with histopathology domain-specific and non-domain-specific (real-world images) pretrained weights.
no code implementations • 6 Jul 2023 • Mokshagna Sai Teja Karanam, Tushar Kataria, Krithika Iyer, Shireen Elhabian
However, these augmentation methods focus on shape augmentation, whereas deep learning models exhibit image-based texture bias resulting in sub-optimal models.
1 code implementation • 23 May 2023 • Jadie Adams, Shireen Elhabian
We present Point2SSM, a novel unsupervised learning approach for constructing correspondence-based statistical shape models (SSMs) directly from raw point clouds.
1 code implementation • 13 May 2023 • Krithika Iyer, Shireen Elhabian
Statistical shape modeling is the computational process of discovering significant shape parameters from segmented anatomies captured by medical images (such as MRI and CT scans), which can fully describe subject-specific anatomy in the context of a population.
1 code implementation • 9 May 2023 • Jadie Adams, Shireen Elhabian
Statistical Shape Modeling (SSM) is a valuable tool for investigating and quantifying anatomical variations within populations of anatomies.
1 code implementation • 9 May 2023 • Jadie Adams, Shireen Elhabian
We derive a fully Bayesian VIB formulation and demonstrate the efficacy of two scalable implementation approaches: concrete dropout and batch ensemble.
no code implementations • 9 May 2023 • Tushar Kataria, Beatrice Knudsen, Shireen Elhabian
Nonetheless, they often fail to generalize when there is a significant domain (i. e., distributional) shift between the training (i. e., source) data and the dataset(s) encountered when deployed (i. e., target), necessitating manual annotations for the target data to achieve acceptable performance.
no code implementations • 6 Sep 2022 • Jadie Adams, Nawazish Khan, Alan Morris, Shireen Elhabian
Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM).
no code implementations • 6 Sep 2022 • Krithika Iyer, Alan Morris, Brian Zenger, Karthik Karanth, Benjamin A Orkild, Oleksandre Korshak, Shireen Elhabian
This paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that capture morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population.
no code implementations • 25 May 2022 • Krithika Iyer, Riddhish Bhalodia, Shireen Elhabian
With extensive experimentation on synthetic and public image datasets, we show that the proposed model learns the relevant latent bottleneck dimensionality without compromising the representation and generation quality of the samples.
no code implementations • 13 May 2022 • Jadie Adams, Shireen Elhabian
This constraint restricts the learning task to solely estimating pre-defined shape descriptors from 3D images and imposes a linear relationship between this shape representation and the output (i. e., shape) space.
no code implementations • 10 Jan 2022 • Wenzheng Tao, Riddhish Bhalodia, Shireen Elhabian
The proposed joint model serves a dual purpose; the world correspondences can directly be used for shape analysis applications, circumventing the heavy pre-processing and segmentation involved in traditional PDM models.
1 code implementation • 13 Nov 2021 • Riddhish Bhalodia, Shireen Elhabian, Ladislav Kavan, Ross Whitaker
We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well.
no code implementations • 14 Oct 2021 • Riddhish Bhalodia, Shireen Elhabian, Jadie Adams, Wenzheng Tao, Ladislav Kavan, Ross Whitaker
Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images.
no code implementations • 28 Oct 2020 • Surojit Saha, Shireen Elhabian, Ross T. Whitaker
Using the proposed method, we enforce the latent representation of an autoencoder to match a target distribution in a learning framework that we call a {\em generative encoding network}.
no code implementations • 13 Jul 2020 • Jadie Adams, Riddhish Bhalodia, Shireen Elhabian
Statistical shape modeling (SSM) has recently taken advantage of advances in deep learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy segmentation, shape registration, and the optimization of population-level shape representations.
no code implementations • 24 Nov 2019 • Riddhish Bhalodia, Iain Lee, Shireen Elhabian
This decoupling enables the use of VAE regularizers on the representation space without impacting the distribution used for sample generation, and thereby reaping the representation learning benefits of the regularizations without sacrificing the sample generation.
no code implementations • 28 Jun 2019 • Alessandro Ferrero, Shireen Elhabian, Ross Whitaker
Generative adversarial networks (GANs) have proven effective in modeling distributions of high-dimensional data.
no code implementations • 13 Jun 2019 • Riddhish Bhalodia, Shireen Elhabian, Ladislav Kavan, Ross Whitaker
Deep networks are an integral part of the current machine learning paradigm.
no code implementations • 6 Mar 2019 • Tim Sodergren, Riddhish Bhalodia, Ross Whitaker, Joshua Cates, Nassir Marrouche, Shireen Elhabian
Here, we propose a maximum-a-posteriori formulation that relies on a generative image model by incorporating both local intensity and global shape priors.
no code implementations • 3 Oct 2018 • Anupama Goparaju, Ibolya Csecs, Alan Morris, Evgueni Kholmovski, Nassir Marrouche, Ross Whitaker, Shireen Elhabian
Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes.
no code implementations • 30 Sep 2018 • Riddhish Bhalodia, Anupama Goparaju, Tim Sodergren, Alan Morris, Evgueni Kholmovski, Nassir Marrouche, Joshua Cates, Ross Whitaker, Shireen Elhabian
In this paper, we propose a machine learning approach that uses deep networks to estimate AF recurrence by predicting shape descriptors directly from MRI images, with NO image pre-processing involved.
no code implementations • CVPR 2017 • Shireen Elhabian, Ross Whitaker
Learning generative shape models from grid-structured representations, aka silhouettes, is usually hindered by (1) data likelihoods with intractable marginals and posteriors, (2) high-dimensional shape spaces with limited training samples (and the associated risk of overfitting), and (3) estimation of hyperparameters relating to model complexity that often entails computationally expensive grid searches.