The 2022 Intracranial Hemorrhage Segmentation Challenge on Non-Contrast head CT (NCCT)
Creators: name: Xiangyu Li
name: Kuanquan Wang
name: Jinbo Liu
name: Hongyu Wang
name: Mingwang Xu
name: Xinjie Liang
Publisher: name: Zenodo
Year: 2022
Subjects: Intracranial hemorrhage (ICH)
Non-Contrast head CT (NCCT)
Segmentation
MICCAI
Challenge

description: Intracranial hemorrhage (ICH) is a common stroke type and has the highest mortality rate among all stroke types [1]. Early and accurate diagnosis of the ICH is critical for saving patients' lives. In regular clinical practice, Non-Contrast Computed Tomography (NCCT) is the most widely used modality for diagnosing ICH due to its fast acquisition and availability in most emergency departments [2]. In clinical diagnosis procedures, accurately estimating the volume of intracranial hemorrhage is significant for predicting hematoma progression and early mortality [3]. The hematoma volume can be estimated by manually delineating the ICH region by radiologists, which is time-consuming [4] and suffers from inter-rater variability [5]. The ABC/2 method [6] is widely adopted in clinical practice to estimate hemorrhage volume for its ease of use. However, the ABC/2 method shows significant<br> volume estimation error, especially for those hemorrhages with irregular shapes [7]. Hence, it is necessary to establish a fully-automated segmentation method, which allows accurate and rapid volume quantification of the intracranial hemorrhage. However, it is still challenging to accurately segment the ICH for automatic methods because ICH exhibits large variations in shapes and locations, and has blurred boundaries [8]. Therefore, it is significant to propose a challenge to advance the novel automatic segmentation methods for accurate intracranial hemorrhage segmentations on NCCT images. However, there are no such hallenges<br> available right now. Thus, we intend to host the first Intracranial Hemorrhage Segmentation Challenge on Noncontrast head CT (Named INSTANCE 2022), which will be served as a solid benchmark for Intracranial hemorrhage Segmentation tasks. <br> Specifically, we have collected 200 3D volumes with refined labeling from 10 experienced radiologists, 100 for the training dataset, 70 for the closed testing dataset, and 30 for the opened validated dataset. DSC, HD, RVD are adopted as evaluation metrics for segmentation. This challenge will also promote intracranial hemorrhage treatment, interactions between researchers, and interdisciplinary communication. References [1] C. J. van Asch, M. J. Luitse, G. J. Rinkel, I. van der Tweel, A. Algra, and C. J. Klijn, “Incidence, case fatality, and<br> functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: A systematic<br> review and meta-analysis,” Lancet. Neurol., vol. 9, no. 2, pp. 167–176, Feb. 2010.<br> [2] J. N. Goldstein and A. J. Gilson, “Critical care management of acute intracerebral hemorrhage,” Curr. Treat.<br> Option. Neurol., vol. 13, no. 2, pp. 204–216, Jan. 2011.<br> [3] J. P. Broderick, T. G. Brott, J. E. Duldner, T. Tomsick, and G. Huster, “Volume of intracerebral hemorrhage. A<br> powerful and easy-to-use predictor of 30-day mortality,” Stroke, vol. 24, no. 7, pp. 987–993, Jan. 1993.<br> [4] K. B. Prakash, S. Zhou, T. C. Morgan, D. F. Hanley, and W. L. Nowinski, “Segmentation and quantification of<br> intra-ventricular/cerebral hemorrhage inCTscans bymodified distance regularized level set evolution technique,”<br> Int. J. Comput. Assist. Radiol. Surg., vol. 7, no. 5, pp. 785–798, Feb. 2012.<br> [5] M. Islam, P. Sanghani, A. A. Q. See, M. L. James, N. K. K. King, and H.Ren, “ICHNet: Intracerebral hemorrhage<br> (ICH) segmentation using deep learning,” in Proc. Int. MICCAI Brainlesion Workshop, Cham, Switzerland: Springer,<br> 2018, pp. 456–463.<br> [6] R. U. Kothari et al., “The ABCs of measuring intracerebral hemorrhage volumes,” Stroke, vol. 27, no. 8, pp.<br> 1304–1305, Aug. 1996.<br> [7] A. J. Webb et al. “Accuracy of the ABC/2 score for intracerebral hemorrhage: Systematic review and analysis of<br> MISTIE, CLEAR-IVH, and CLEAR III,” Stroke, vol. 46, no. 9, pp. 2470–2476, Aug. 2015.<br> [8] J. Cho et al., “Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using<br> cascaded deep learning models,” J.Digit. Imag., vol. 32, no. 3, pp. 450–461, Jan. 2019