Skip to content

CSA-SegNet: enhancing ovarian tumor segmentation from ultrasound images with spatial pooling and contour-based loss functions

Jul 2026 · International Journal of Machine Learning and Cybernetics · Vol 17 · 0 citations · 57 references
Computer Science

TL;DR

A new loss function is proposed that enhances conventional loss functions such as IoU loss, SSIM Loss, and Focal loss function with Active Contour Euler Elastical loss with effectiveness in enhancing segmentation accuracy and boundary delineation of ovarian ultrasound images.

View source

Similar papers

Conference Aug 2026

Research on breast cancer image segmentation method based on improved ResNet34-UNet hybrid loss function

The proposed improved U-Net-based deep learning model for image segmentation provides an effective solution for automated, high-precision segmentation of breast ultrasound images, demonstrating considerable potential for clinical translation.

Bo-Chao Zou, Shuangde Li, Ye-Rong Zhang · 0 citations
Open access Sep 2026

DS-UNet: enhancing liver tumor segmentation in CT images via dense state space selection and gating mechanism

Dense State Space Selection U-Net, a dense state space selection network, is proposed, to enhance liver tumor segmentation from CT images, by integrating a gating mechanism and dense state space blocks, resulting in superior segmentation accuracy.

Xue-Ying Fan, Wan-Bin Lin, Li-Ming Xu et al. · 0 citations
Open access Aug 2026

DCLA-Net: dual-encoder cross-level alignment network for ultrasound-based ectopic pregnancy mass segmentation

The results demonstrate that the dual-encoder architecture, MSCAFM, and DAFM all contribute positively to the overall segmentation performance, and DCLA-Net consistently achieves superior performance across all evaluation metrics on both datasets.

Yan Cheng, Xiao-Kang Ding, Wei-Wei Zhu · 0 citations
Open access Aug 2026

Attention-Guided Mask R-CNN for Liver Tumor Segmentation and Classification from CT Scans: A Tailored Approach

An attention-controlled automated liver tumor segmentation and classification based on a customized Mask Region Convolutional Neural Network with an addition of Multi-Scale Attention Gate (MSAG) is introduced with affirm the usefulness and clinical appropriateness of the suggested framework.

Babeetta Bbhagat, Mohini Kumbhar, Swati Powar et al. · 0 citations
Open access 2026

NeuroFocusNet: An Attention-Enhanced 3-D U-Net for Multimodal MRI Brain Tumor Segmentation

A 3D U-Net-based segmentation framework, NeuroFocusNet, which synchronizes spatial attention, channel recalibration, and attention-gated skip connections within a single volumetric encoder-decoder model is proposed, which is encouraging when considering the feasibility of using NeuroFocusNet as a technically promising...

Faizan Ullah, Z. Abbas, Sergo Gegechkori et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.