Enhancing Semantic Segmentation with Adaptive Focal Loss: A Novel Approach

Deep learning has achieved outstanding accuracy in medical image segmentation, particularly for objects like organs or tumors with smooth boundaries or large sizes. Whereas, it encounters significant difficulties with objects that have zigzag boundaries or are small in size, leading to a notable decrease in segmentation effectiveness. In this context, using a loss function that incorporates smoothness and volume information into a model's predictions offers a promising solution to these shortcomings. In this work, we introduce an Adaptive Focal Loss (A-FL) function designed to mitigate class imbalance by down-weighting the loss for easy examples that results in up-weighting the loss for hard examples and giving greater emphasis to challenging examples, such as small and irregularly shaped objects. The proposed A-FL involves dynamically adjusting a focusing parameter based on an object's surface smoothness, size information, and adjusting the class balancing parameter based on the ratio of targeted area to total area in an image. We evaluated the performance of the A-FL using ResNet50-encoded U-Net architecture on the Picai 2022 and BraTS 2018 datasets. On the Picai 2022 dataset, the A-FL achieved an Intersection over Union (IoU) of 0.696 and a Dice Similarity Coefficient (DSC) of 0.769, outperforming the regular Focal Loss (FL) by 5.5% and 5.4% respectively. It also surpassed the best baseline Dice-Focal by 2.0% and 1.2%. On the BraTS 2018 dataset, A-FL achieved an IoU of 0.883 and a DSC of 0.931. The comparative studies show that the proposed A-FL function surpasses conventional methods, including Dice Loss, Focal Loss, and their hybrid variants, in IoU, DSC, Sensitivity, and Specificity metrics. This work highlights A-FL's potential to improve deep learning models for segmenting clinically significant regions in medical images, leading to more precise and reliable diagnostic tools.

Uncertainty Driven Bottleneck Attention U-net for OAR Segmentation

Organ at risk (OAR) segmentation in computed tomography (CT) imagery is a difficult task for automated segmentation methods and can be crucial for downstream radiation treatment planning. U-net has become a de-facto standard for medical image segmentation and is frequently used as a common baseline in medical image segmentation tasks. In this paper, we develop a multiple decoder U-net architecture where a noisy auxiliary decoder is used to generate noisy segmentation. The segmentation from the main branch and the noisy segmentation from the auxiliary branch are used together to estimate the attention. Our contribution is the development of a new attention module which derives the attention from the softmax probabilities of two decoder branches. The union and intersection of two segmentation masks from two branches carry the information where both decoders agree and disagree. The softmax probabilities from regions of agreement and disagreement are the indicators of low and high uncertainty. Thus, the probabilities of those selected regions are used as attention in the bottleneck layer of the encoder and passes only through the main decoder for segmentation. For accurate contour segmentation, we also developed a CT intensity integrated regularization loss. We tested our model on two publicly available OAR challenge datasets, Segthor and LCTSC respectively. We trained 12 models on each dataset with and without the proposed attention model and regularization loss to check the effectiveness of the attention module and the regularization loss. The experiments demonstrate a clear accuracy improvement (2\% to 5\% Dice) on both datasets. Code for the experiments will be made available upon the acceptance for publication.