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.

The recent application of Deep Learning in various areas of medical image analysis has brought excellent performance gain. The application of deep learning technologies in medical image registration successfully outperformed traditional optimization based registration algorithms both in registration time and accuracy. In this paper, we present a densely connected convolutional architecture for deformable image registration. The training of the network is unsupervised and does not require ground-truth deformation or any synthetic deformation as a label. The proposed architecture is trained and tested on two different version of tissue cleared data, 10\% and 25\% resolution of high resolution dataset respectively and demonstrated comparable registration performance with the state-of-the-art ANTS registration method. The proposed method is also compared with the deep-learning based Voxelmorph registration method. Due to the memory limitation, original voxelmorph can work at most 15\% resolution of Tissue cleared data. For rigorous experimental comparison we developed a patch-based version of Voxelmorph network, and trained it on 10\% and 25\% resolution. In both resolution, proposed DenseDeformation network outperformed Voxelmorph in registration accuracy.