Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning
Nerve segmentation is crucial in medical imaging for precise identification of nerve structures. This study presents an optimized DeepLabV3-based segmentation pipeline that incorporates automated threshold fine-tuning to improve segmentation accuracy. By refining preprocessing steps and implementing parameter optimization, we achieved a Dice Score of 0.78, an IoU of 0.70, and a Pixel Accuracy of 0.95 on ultrasound nerve imaging. The results demonstrate significant improvements over baseline models and highlight the importance of tailored parameter selection in automated nerve detection.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks
We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions. MedicDeepLabv3+ improves t…
GPUSkull StrippingDooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation
In this paper we present a new approach for feature fusion between RGB and LWIR Thermal images for the task of semantic segmentation for driving perception. We propose DooDLeNet, a double DeepLab architecture with specia…
DecoderSegmentationSemantic SegmentationThermal Image SegmentationPolyp segmentation in colonoscopy images using DeepLabV3++
Segmenting polyps in colonoscopy images is essential for the early identification and diagnosis of colorectal cancer, a significant cause of worldwide cancer deaths. Prior deep learning based models such as Attention bas…
DecoderSegmentationEvaluating Deep Learning-Based Nerve Segmentation in Brachial Plexus Ultrasound Under Realistic Data Constraints
Accurate nerve localization is critical for the success of ultrasound-guided regional anesthesia, yet manual identification remains challenging due to low image contrast, speckle noise, and inter-patient anatomical varia…
Panoptic-DeepLab
We present Panoptic-DeepLab, a bottom-up and single-shot approach for panoptic segmentation. Our Panoptic-DeepLab is conceptually simple and delivers state-of-the-art results. In particular, we adopt the dual-ASPP and du…
DecoderInstance SegmentationPanoptic Segmentationregression+2