Search Results for author: Demetri Terzopoulos

Found 25 papers, 5 papers with code

CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI

no code implementations29 Mar 2022 Alex Ling Yu Hung, Haoxin Zheng, Qi Miao, Steven S. Raman, Demetri Terzopoulos, Kyunghyun Sung

However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices.

Neuromuscular Control of the Face-Head-Neck Biomechanical Complex With Learning-Based Expression Transfer From Images and Videos

no code implementations12 Nov 2021 Xiao S. Zeng, Surya Dwarakanath, Wuyue Lu, Masaki Nakada, Demetri Terzopoulos

The success of our approach is demonstrated through experiments involving the transfer onto our face-head-neck model of facial expressions and head poses from a range of facial images and videos.

A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation

1 code implementation18 Mar 2021 Qinji Yu, Kang Dang, Nima Tajbakhsh, Demetri Terzopoulos, Xiaowei Ding

Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data.

Medical Image Segmentation Semantic Segmentation

MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images

1 code implementation28 Oct 2020 Ayaan Haque, Abdullah-Al-Zubaer Imran, Adam Wang, Demetri Terzopoulos

Our extensive experimentation with varied quantities of labeled data in the training sets justify the effectiveness of our multitasking model for the classification of pneumonia and segmentation of lungs from chest X-ray images.

General Classification

End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery

no code implementations ECCV 2020 Ali Hatamizadeh, Debleena Sengupta, Demetri Terzopoulos

The automated segmentation of buildings in remote sensing imagery is a challenging task that requires the accurate delineation of multiple building instances over typically large image areas.

Semantic Segmentation

Progressive Adversarial Semantic Segmentation

no code implementations8 May 2020 Abdullah-Al-Zubaer Imran, Demetri Terzopoulos

Medical image computing has advanced rapidly with the advent of deep learning techniques such as convolutional neural networks.

Lesion Segmentation Semantic Segmentation

Partly Supervised Multitask Learning

no code implementations5 May 2020 Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang, Wei Fan, Yuan Xiao, Dingjun Hao, Zhen Qian, Demetri Terzopoulos

Leveraging self-supervision and adversarial training, we propose a novel general purpose semi-supervised, multiple-task model---namely, self-supervised, semi-supervised, multitask learning (S$^4$MTL)---for accomplishing two important tasks in medical imaging, segmentation and diagnostic classification.

Medical Image Segmentation

Analysis of Scoliosis From Spinal X-Ray Images

no code implementations15 Apr 2020 Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang, Wei Fan, Kenneth M. C. Cheung, Michael To, Zhen Qian, Demetri Terzopoulos

Leveraging a carefully-adjusted U-Net model with progressive side outputs, we propose an end-to-end segmentation model that provides a fully automatic and reliable segmentation of the vertebrae associated with scoliosis measurement.

Image Segmentation Using Deep Learning: A Survey

2 code implementations15 Jan 2020 Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, Demetri Terzopoulos

Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others.

Image Compression Scene Understanding +1

End-to-End Deep Convolutional Active Contours for Image Segmentation

no code implementations29 Sep 2019 Ali Hatamizadeh, Debleena Sengupta, Demetri Terzopoulos

The Active Contour Model (ACM) is a standard image analysis technique whose numerous variants have attracted an enormous amount of research attention across multiple fields.

Instance Segmentation Semantic Segmentation

End-to-End Boundary Aware Networks for Medical Image Segmentation

no code implementations21 Aug 2019 Ali Hatamizadeh, Demetri Terzopoulos, Andriy Myronenko

Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation.

Brain Tumor Segmentation Tumor Segmentation

Deep Active Lesion Segmentation

1 code implementation19 Aug 2019 Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel Rubin, Demetri Terzopoulos

Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors.

Lesion Segmentation

Semi-Supervised Multi-Task Learning With Chest X-Ray Images

no code implementations10 Aug 2019 Abdullah-Al-Zubaer Imran, Demetri Terzopoulos

Discriminative models that require full supervision are inefficacious in the medical imaging domain when large labeled datasets are unavailable.

Multi-Task Learning

Multi-Adversarial Variational Autoencoder Networks

no code implementations14 Jun 2019 Abdullah-Al-Zubaer Imran, Demetri Terzopoulos

The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform image-based unsupervised clustering or semi-supervised classification.

General Classification Image Generation +1

Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels

no code implementations28 May 2019 Ali Hatamizadeh, Hamid Hosseini, Zhengyuan Liu, Steven D. Schwartz, Demetri Terzopoulos

The reliable segmentation of retinal vasculature can provide the means to diagnose and monitor the progression of a variety of diseases affecting the blood vessel network, including diabetes and hypertension.

Retinal Vessel Segmentation

Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network

no code implementations18 Feb 2019 Abdullah-Al-Zubaer Imran, Ali Hatamizadeh, Shilpa P. Ananth, Xiaowei Ding, Demetri Terzopoulos, Nima Tajbakhsh

We evaluated our model using 84 chest CT scans from the LIDC and 154 pathological cases from the LTRC datasets.

Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data

no code implementations25 Jan 2019 Nima Tajbakhsh, Yufei Hu, Junli Cao, Xingjian Yan, Yi Xiao, Yong Lu, Jianming Liang, Demetri Terzopoulos, Xiaowei Ding

We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data.

Colorization Transfer Learning

Attention-based Natural Language Person Retrieval

no code implementations24 May 2017 Tao Zhou, Muhao Chen, Jie Yu, Demetri Terzopoulos

Following the recent progress in image classification and captioning using deep learning, we develop a novel natural language person retrieval system based on an attention mechanism.

Image Classification Person Retrieval +1

Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars

no code implementations1 Apr 2017 Chenfanfu Jiang, Siyuan Qi, Yixin Zhu, Siyuan Huang, Jenny Lin, Lap-Fai Yu, Demetri Terzopoulos, Song-Chun Zhu

We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms.

Scene Understanding Semantic Segmentation

Inferring Forces and Learning Human Utilities From Videos

no code implementations CVPR 2016 Yixin Zhu, Chenfanfu Jiang, Yibiao Zhao, Demetri Terzopoulos, Song-Chun Zhu

We propose a notion of affordance that takes into account physical quantities generated when the human body interacts with real-world objects, and introduce a learning framework that incorporates the concept of human utilities, which in our opinion provides a deeper and finer-grained account not only of object affordance but also of people's interaction with objects.

Motion Planning Robot Task Planning

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