Robust T-Loss for Medical Image Segmentation

Gonzalez-Jimenez, Alvaro; Lionetti, Simone; Gottfrois, Philippe; Gröger, Fabian; Pouly, Marc; Navarini, Alexander A.

doi:10.1007/978-3-031-43898-1_68

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…For medical image segmentation tasks, a combination of Robust TLoss [53] (Truncated L1 Loss) and Dice Loss [54] can be used as the loss function. This combined loss function aims to leverage the strengths of both loss functions: Robust TLoss for handling outliers and Dice Loss for optimizing the overlap between the predicted and ground truth segmentation masks.…”

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confidence: 99%

Automated Left Ventricle Segmentation in Echocardiography using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment

Balasubramani,

Sung,

Hsieh

et al. 2024

Preprint

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Accurate segmentation of the Left Ventricle (LV) in Echocardiogram (Echo) images is essential for cardiovascular analysis. Conventional techniques are labor-intensive and exhibit inter-observer variability. Deep learning has emerged as a powerful tool for automated medical image segmentation, offering advantages in speed and potentially superior accuracy. This study explores the efficacy of employing the YOLO (You Only Look Once) segmentation model for automated LV segmentation in Echo images. YOLO, a cutting-edge object detection model, achieves exceptional speed-accuracy balance through its well-designed architecture. It utilizes efficient dilated convolutional layers and bottleneck blocks for feature extraction, while incorporating innovations like path aggregation and spatial attention mechanisms. These attributes make YOLO a compelling candidate for adaptation to LV segmentation in Echo images. We posit that by fine-tuning a pre-trained YOLO based model on a well-annotated Echo image dataset, we can leverage the model's strengths in real-time processing and precise object localization to achieve robust LV segmentation. The proposed approach entails fine-tuning a pre-trained YOLO model on a rigorously labeled Echo image dataset. Model performance has been evaluated using established metrics such as mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 50% (mAP50) with 98.31%and across a range of IoU thresholds from 50% to 95% (mAP50:95) with 75.27%. Successful implementation of YOLO for LV segmentation has the potential to significantly expedite and standardize Echo image analysis. This advancement could translate to improved clinical decision-making and enhanced patient care.

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confidence: 99%