Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss
Accurately segmenting a variety of clinically significant lesions from whole body computed tomography (CT) scans is a critical task on precision oncology imaging, denoted as universal lesion segmentation (ULS). Manual annotation is the current clinical practice, being highly time-consuming and inconsistent on tumor's longitudinal assessment. Effectively training an automatic segmentation model is desirable but relies heavily on a large number of pixel-wise labelled data. Existing weakly-supervised segmentation approaches often struggle with regions nearby the lesion boundaries. In this paper, we present a novel weakly-supervised universal lesion segmentation method by building an attention enhanced model based on the High-Resolution Network (HRNet), named AHRNet, and propose a regional level set (RLS) loss for optimizing lesion boundary delineation. AHRNet provides advanced high-resolution deep image features by involving a decoder, dual-attention and scale attention mechanisms, which are crucial to performing accurate lesion segmentation. RLS can optimize the model reliably and effectively in a weakly-supervised fashion, forcing the segmentation close to lesion boundary. Extensive experimental results demonstrate that our method achieves the best performance on the publicly large-scale DeepLesion dataset and a hold-out test set.
Code (0)
등록된 구현이 없습니다.
Tasks
Computed Tomography (CT)DecoderLesion SegmentationSegmentationWeakly supervised segmentationSimilar Papers 제목 키워드 기반
RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training
As an essential indicator for cancer progression and treatment response, tumor size is often measured following the response evaluation criteria in solid tumors (RECIST) guideline in CT slices. By marking each lesion wit…
Lesion SegmentationWeakly supervised segmentationSupervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images
Lesion segmentation is the first step in most automatic melanoma recognition systems. Deficiencies and difficulties in dermoscopic images such as color inconstancy, hair occlusion, dark corners and color charts make lesi…
Lesion SegmentationSaliency DetectionSegmentationSemantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation
Weakly-supervised learning under image-level labels supervision has been widely applied to semantic segmentation of medical lesions regions. However, 1) most existing models rely on effective constraints to explore the i…
Pseudo LabelSegmentationSemantic SegmentationWeakly-supervised LearningImproving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation
Automated lesion segmentation of medical images has made tremendous improvements in recent years due to deep learning advancements. However, accurately capturing fine-grained global and regional feature representations r…
Image SegmentationLesion SegmentationMedical Image SegmentationSegmentation+2Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation
Ultrasound imaging plays a critical role in the early detection of breast cancer. Accurate identification and segmentation of lesions are essential steps in clinical practice, requiring methods to assist physicians in le…
Lesion SegmentationSegmentationWeakly-supervised Learning