2024-01-23-pang24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Prior Guided 3D Medical Image Landmark Localization	Accurate detection of 3D landmarks is critical for evaluating and characterizing anatomical features and performing preoperative diagnostic screening. However, detecting 3D landmarks can be challenging due to the local structural homogeneity of medical images. To address this issue, physicians often annotate multiple landmarks in a single slice, particularly when estimating 3D distance or volume. In this study, we present a prior guided coarse-to-fine framework for efficient and accurate 3D medical landmark detection; we make use of the prior information that in specific settings physicians annotate multiple landmarks on a same slice. The coarse stage uses coordinate regression on downsampled 3D images to maintain the structural relationships across different landmarks. The fine stage categorizes landmarks as correlated and independent landmarks based on their annotation prior. For independent landmarks, we train multiple models to capture local features and ensure reliable local predictions. For correlated landmarks, we mimic the manual annotation process and propose a correlated landmark detection model that merges information from various patches to query key slices and identify correlated landmarks. Our method is extensively evaluated on two datasets, exhibiting superior performance with an average detection error of respective 3.29 mm and 2.13 mm.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	pang24a	0	Prior Guided 3D Medical Image Landmark Localization	1163	1175	1163-1175	1163	false	Pang, Yijie and Cheng, Pujin and Lyu, Junyan and Lin, Fan and Tang, Xiaoying	given family Yijie Pang given family Pujin Cheng given family Junyan Lyu given family Fan Lin given family Xiaoying Tang	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/pang24a/pang24a.pdf