Abstract:
Prostate tumor segmentation from multi-modality magnetic resonance (MR) images is indispensable for diagnosis and treatment of prostate cancer. Existing methods typically...Show MoreMetadata
Abstract:
Prostate tumor segmentation from multi-modality magnetic resonance (MR) images is indispensable for diagnosis and treatment of prostate cancer. Existing methods typically over-look the specific distribution of prostate tumors in MR images and also the efficacy of extracting features from different MR modalities in the tumor segmentation task. In this paper, we address these limitations by proposing a novel localization-to-segmentation framework. First, we design a localization stage to locate tumor slices precisely via a label order consistency (LOC) strategy, by explicitly utilizing tumor distribution prior. Then, in the subsequent segmentation stage, we develop an attention-based multi-modality collaborative learning (MCL) module, to extract high-level modality-specific features while focusing on aggregating complementary features across modalities, for segmenting tumors from the localized tumor slices. Experimental results demonstrate that our method achieves state-of-the-art segmentation performance on an in-house prostate MRI dataset, especially for tumors with low contrast to the surrounding tissues.
Date of Conference: 18-21 April 2023
Date Added to IEEE Xplore: 01 September 2023
ISBN Information: