# 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes Source: [3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:55:49.570Z Authors: Ken C. L. Wong, Mehdi Moradi, Hui Tang, Tanveer Syeda-Mahmood Published: 2018-08-31T22:42:12.000Z ## Abstract With the introduction of fully convolutional neural networks, deep learning has raised the benchmark for medical image segmentation on both speed and accuracy, and different networks have been proposed for 2D and 3D segmentation with promising results. Nevertheless, most networks only handle relatively small numbers of labels (\<10), and there are very limited works on handling highly unbalanced object sizes especially in 3D segmentation. In this paper, we propose a network architecture and the corresponding loss function which improve segmentation of very small structures. By combining skip connections and deep supervision with respect to the computational feasibility of 3D segmentation, we propose a fast converging and computationally efficient network architecture for accurate segmentation. Furthermore, inspired by the concept of focal loss, we propose an exponential logarithmic loss which balances the labels not only by their relative sizes but also by their segmentation difficulties. We achieve an average Dice coefficient of 82% on brain segmentation with 20 labels, with the ratio of the smallest to largest object sizes as 0.14%. Less than 100 epochs are required to reach such accuracy, and segmenting a 128x128x128 volume only takes around 0.4 s. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Brain Tumor Segmentation on BraTS Downsampling Factor 1 2019](): Brain Tumor Segmentation; BraTS Downsampling Factor 1 2019; Dice WT: 88.8. - [Brain Tumor Segmentation on Multi-modal brain tumor segmentation dataset (test)](): Brain Tumor Segmentation; Multi-modal brain tumor segmentation dataset (test); Latency (s): 0.33. - [Brain Tumor Segmentation on BraTS Downsampling Factor 3 2019](): Brain Tumor Segmentation; BraTS Downsampling Factor 3 2019; Dice WT: 70.6. ## Original sources and implementation [Original paper]() [PDF]()