Intracranial Hemorrhage Segmentation

Intracranial hemorrhage (ICH) segmentation aims to automatically identify and delineate bleeding regions in brain CT scans, aiding in rapid diagnosis and treatment planning for stroke. Current research focuses on improving segmentation accuracy and efficiency using various deep learning architectures, including U-Net variations, Swin Transformers, and the Segment Anything Model (SAM), often employing weakly supervised or semi-supervised learning techniques to reduce reliance on extensive labeled datasets. These advancements hold significant promise for improving the speed and accuracy of ICH diagnosis, ultimately leading to better patient outcomes and streamlining clinical workflows. The development of robust and reliable segmentation methods is a key area of ongoing investigation, with recent challenges highlighting the need for handling anisotropic data and addressing the inherent uncertainties in medical image analysis.

Papers

November 7, 2024

ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism
Xinlei Yu, Ahmed Elazab, Ruiquan Ge, Hui Jin, Xinchen Jiang, Gangyong Jia, Qing Wu, Qinglei Shi, Changmiao Wang
Segmentation Task Prognosis Prediction Classification Network Intracerebral Hemorrhage Intracranial Hemorrhage Multi Task Network Intracranial Hemorrhage Segmentation Multi Task Loss

July 29, 2024

Weakly Supervised Intracranial Hemorrhage Segmentation with YOLO and an Uncertainty Rectified Segment Anything Model
Pascal Spiegler, Amirhossein Rasoulian, Yiming Xiao
Segment Anything Model High Uncertainty Anticipation Bounding Box Segmentation Result YOLO Object YOLO Shake Hand Intracranial Hemorrhage Intracranial Hemorrhage Segmentation

January 16, 2024

Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection
Cooper Gamble, Shahriar Faghani, Bradley J. Erickson
Deep Learning Conformal Prediction Uncertainty Aware Deep Learning Intracranial Hemorrhage Segmentation Intracranial Hemorrhage Detection

January 9, 2024

An Automatic Cascaded Model for Hemorrhagic Stroke Segmentation and Hemorrhagic Volume Estimation
Weijin Xu, Zhuang Sha, Huihua Yang, Rongcai Jiang, Zhanying Li, Wentao Liu, Ruisheng Su
Cascade Model Intracerebral Hemorrhage Intracranial Hemorrhage Segmentation Hemorrhage Area

November 14, 2023

SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation
Yinuo Wang, Kai Chen, Weimin Yuan, Cai Meng, XiangZhi Bai
Loss Function Medical Image Segmentation Segment Anything Model Adaptation Concern Intracranial Hemorrhage Segmentation

September 28, 2023

Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images
Shreyas H Ramananda, Vaanathi Sundaresan
True Class Non Contrast Intracerebral Hemorrhage Intracranial Hemorrhage Intracranial Hemorrhage Segmentation

August 22, 2023

BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Biao Wu, Yutong Xie, Zeyu Zhang, Jinchao Ge, Kaspar Yaxley, Suzan Bahadir, Qi Wu, Yifan Liu, Minh-Son To
Intracerebral Hemorrhage Intracranial Hemorrhage Intracranial Hemorrhage Segmentation Intracranial Hemorrhage Detection

April 11, 2023

Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning
Amirhossein Rasoulian, Soorena Salari, Yiming Xiao
Swin Transformer Intracerebral Hemorrhage Intracranial Hemorrhage Category Learning Intracranial Hemorrhage Segmentation

February 24, 2023

Automatic Classification of Symmetry of Hemithoraces in Canine and Feline Radiographs
Peyman Tahghighi, Nicole Norena, Eran Ukwatta, Ryan B Appleby, Amin Komeili
Cyclic Symmetry Automatic Classification PET Dog Shoulder Radiograph Intracranial Hemorrhage Segmentation

January 9, 2023

The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
Xiangyu Li, Gongning Luo, Kuanquan Wang, Hongyu Wang, Jun Liu, Xinjie Liang, Jie Jiang, Zhenghao Song, Chunyue Zheng, Haokai Chi, Mingwang Xu, Yingte He, Xinghua Ma, Jingwen Guo, Yifan Liu, Chuanpu Li, Zeli Chen, Md Mahfuzur Rahman Siddiquee, Andriy Myronenko, Antoine P. Sanner, Anirban Mukhopadhyay, Ahmed E. Othman, Xingyu Zhao, Weiping Liu, Jinhuang Zhang, Xiangyuan Ma, Qinghui Liu, Bradley J. MacIntosh, Wei Liang, Moona Mazher, Abdul Qayyum, Valeriia Abramova, Xavier Lladó, Shuo Li
Non Contrast Intracranial Hemorrhage Intracranial Hemorrhage Segmentation

November 29, 2022

Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT
Jacopo Teneggi, Paul H. Yi, Jeremias Sulam
Numerical Data Weakly Supervised Learning Manual Label Image Level Annotation Intracranial Hemorrhage Segmentation Intracranial Hemorrhage Detection Head Computed Tomography Hemorrhage Detection

September 21, 2022

Automated segmentation of intracranial hemorrhages from 3D CT
Md Mahfuzur Rahman Siddiquee, Dong Yang, Yufan He, Daguang Xu, Andriy Myronenko
3d Ct Intracerebral Hemorrhage 2 Dimensional Segmentation Intracranial Hemorrhage Segmentation

August 12, 2022

Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT
Qinghui Liu, Bradley J MacIntosh, Till Schellhorn, Karoline Skogen, KyrreEeg Emblem, Atle Bjørnerud
Attention Mechanism Segmentation Task U Net AI Tool 3D Voxels Intracranial Hemorrhage Intracranial Hemorrhage Segmentation Head Computed Tomography

February 14, 2022

A Graphical Approach For Brain Haemorrhage Segmentation
Dr. Ninad Mehendale, Pragya Gupta, Nishant Rajadhyaksha, Ansh Dagha, Mihir Hundiwala, Aditi Paretkar, Sakshi Chavan, Tanmay Mishra
Intracranial Hemorrhage Intracranial Hemorrhage Segmentation Graphical Representation Hemorrhage Area

Intracranial Hemorrhage Segmentation

Papers

ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism

Weakly Supervised Intracranial Hemorrhage Segmentation with YOLO and an Uncertainty Rectified Segment Anything Model

Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection

An Automatic Cascaded Model for Hemorrhagic Stroke Segmentation and Hemorrhagic Volume Estimation

SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation

Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images

BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset

Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning

Automatic Classification of Symmetry of Hemithoraces in Canine and Feline Radiographs

The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge

Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT

Automated segmentation of intracranial hemorrhages from 3D CT

Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT

A Graphical Approach For Brain Haemorrhage Segmentation