Search Results for author: Iuliana Toma-Dasu

Found 3 papers, 2 papers with code

SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

1 code implementation • 15 Dec 2023 • Xiangde Luo, Jia Fu, Yunxin Zhong, Shuolin Liu, Bing Han, Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu, Yiwen Ye, Ziyang Chen, Yong Xia, Yanzhou Su, Jin Ye, Junjun He, Zhaohu Xing, Hongqiu Wang, Lei Zhu, Kaixiang Yang, Xin Fang, Zhiwei Wang, Chan Woong Lee, Sang Joon Park, Jaehee Chun, Constantin Ulrich, Klaus H. Maier-Hein, Nchongmaje Ndipenoch, Alina Miron, Yongmin Li, Yimeng Zhang, Yu Chen, Lu Bai, Jinlong Huang, Chengyang An, Lisheng Wang, Kaiwen Huang, Yunqi Gu, Tao Zhou, Mu Zhou, Shichuan Zhang, Wenjun Liao, Guotai Wang, Shaoting Zhang

The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.

Computed Tomography (CT) Image Segmentation +3

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Code

Fully Automatic Segmentation of Gross Target Volume and Organs-at-Risk for Radiotherapy Planning of Nasopharyngeal Carcinoma

1 code implementation • 4 Oct 2023 • Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu

Target segmentation in CT images of Head&Neck (H&N) region is challenging due to low contrast between adjacent soft tissue.

Benchmarking Segmentation

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AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection

no code implementations • 21 May 2023 • Mehdi Astaraki, Francesca De Benetti, Yousef Yeganeh, Iuliana Toma-Dasu, Örjan Smedby, Chunliang Wang, Nassir Navab, Thomas Wendler

This work intends to, first, propose a robust inpainting model to learn the details of healthy anatomies and reconstruct high-resolution images by preserving anatomical constraints.

Unsupervised Anomaly Detection

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