Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures

by   Yuyin Zhou, et al.

Trauma is the worldwide leading cause of death and disability in those younger than 45 years, and pelvic fractures are a major source of morbidity and mortality. Automated segmentation of multiple foci of arterial bleeding from abdominopelvic trauma CT could provide rapid objective measurements of the total extent of active bleeding, potentially augmenting outcome prediction at the point of care, while improving patient triage, allocation of appropriate resources, and time to definitive intervention. In spite of the importance of active bleeding in the quick tempo of trauma care, the task is still quite challenging due to the variable contrast, intensity, location, size, shape, and multiplicity of bleeding foci. Existing work [4] presents a heuristic rule-based segmentation technique which requires multiple stages and cannot be efficiently optimized end-to-end. To this end, we present, Multi-Scale Attentional Network (MSAN), the first yet reliable end-to-end network, for automated segmentation of active hemorrhage from contrast-enhanced trauma CT scans. MSAN consists of the following components: 1) an encoder which fully integrates the global contextual information from holistic 2D slices; 2) a multi-scale strategy applied both in the training stage and the inference stage to handle the challenges induced by variation of target sizes; 3) an attentional module to further refine the deep features, leading to better segmentation quality; and 4) a multi-view mechanism to fully leverage the 3D information. Our MSAN reports a significant improvement of more than 7 compared to prior arts in terms of DSC.


page 2

page 7


E^2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans

Developing an effective liver and liver tumor segmentation model from CT...

Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation

Multi-view echocardiographic sequences segmentation is crucial for clini...

Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction

This paper presents a unified training strategy that enables a novel mul...

CovTANet: A Hybrid Tri-level Attention Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans

Rapid and precise diagnosis of COVID-19 is one of the major challenges f...

Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images

Multiple myeloma cancer is a type of blood cancer that happens when the ...

Attention Augmented ConvNeXt UNet For Rectal Tumour Segmentation

It is a challenge to segment the location and size of rectal cancer tumo...

CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans

Automatic lung lesions segmentation of chest CT scans is considered a pi...

Please sign up or login with your details

Forgot password? Click here to reset