Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors

Brain tumors are the most common solid tumors and the leading cause of cancer-related death among children. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high inter-operator variability, underscoring the need for more efficient methods. We compared two deep learning-based 3D segmentation models, DeepMedic and nnU-Net, after training with pediatric-specific multi-institutional brain tumor data using based on multi-parametric MRI scans.Multi-parametric preoperative MRI scans of 339 pediatric patients (n=293 internal and n=46 external cohorts) with a variety of tumor subtypes, were preprocessed and manually segmented into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). After training, performance of the two models on internal and external test sets was evaluated using Dice scores, sensitivity, and Hausdorff distance with reference to ground truth manual segmentations. Dice score for nnU-Net internal test sets was (mean +/- SD (median)) 0.9+/-0.07 (0.94) for WT, 0.77+/-0.29 for ET, 0.66+/-0.32 for NET, 0.71+/-0.33 for CC, and 0.71+/-0.40 for ED, respectively. For DeepMedic the Dice scores were 0.82+/-0.16 for WT, 0.66+/-0.32 for ET, 0.48+/-0.27, for NET, 0.48+/-0.36 for CC, and 0.19+/-0.33 for ED, respectively. Dice scores were significantly higher for nnU-Net (p<=0.01). External validation of the trained nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high generalization capability in segmentation of whole tumor and tumor core with Dice scores of 0.87+/-0.13 (0.91) and 0.83+/-0.18 (0.89), respectively. Pediatric-specific data trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors

A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation

In this paper, a Convolutional Neural Network (CNN) system is proposed for brain tumor segmentation. The system consists of three parts, a pre-processing block to reduce the data volume, an application-specific CNN(ASCNN) to segment tumor areas precisely, and a refinement block to detect/remove false positive pixels. The CNN, designed specifically for the task, has 7 convolution layers, 16 channels per layer, requiring only 11716 parameters. The convolutions combined with max-pooling in the first half of the CNN are performed to localize tumor areas. Two convolution modes, namely depthwise convolution and standard convolution, are performed in parallel in the first 2 layers to extract elementary features efficiently. For a fine classification of pixel-wise precision in the second half of the CNN, the feature maps are modulated by adding the individually weighted local feature maps generated in the first half of the CNN. The performance of the proposed system has been evaluated by an online platform with dataset of Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) 2018. Requiring a very low computation volume, the proposed system delivers a high segmentation quality indicated by its average Dice scores of 0.75, 0.88 and 0.76 for enhancing tumor, whole tumor and tumor core, respectively, and also by the median Dice scores of 0.85, 0.92, and 0.86. The consistency in system performance has also been measured, demonstrating that the system is able to reproduce almost the same output to the same input after retraining. The simple structure of the proposed system facilitates its implementation in computation restricted environment, and a wide range of applications can thus be expected

U-Net and its variants for medical image segmentation: theory and applications

U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.Comment: 42 pages, in IEEE Acces

A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation

Brain tumor diagnosis is an important issue in health care. Automated brain tumor segmentation can help timely diagnosis. It is, however, very challenging to achieve high-quality segmentation results, because the shapes, sizes, textures and locations of brain tumors vary from patient to patient. To develop a Convolutional Neural Network (CNN) system for a high-quality brain tumor segmentation at the lowest computation cost, the CNN should be custom-designed to extract efficiently sufficient critical features particularly related to the tumors from brain images for the multi-class segmentation of tumor areas. In this thesis, a CNN system is proposed for brain tumor segmentation. The system consists of three parts, a pre-processing block to reduce the data volume, an application-specific CNN (ASCNN) to segment tumor areas precisely, and a refinement block to detect false positive voxels. The CNN, designed specifically for the task, has 7 convolution layers, and the number of output channels per layer is no more than 16. The convolutions combined with max-pooling in the first half of the CNN are performed to localize brain tumor areas. Two convolution modes, namely depthwise convolution and standard convolution, are performed in parallel in the first 2 layers to extract elementary features efficiently. In the second half of the CNN, the convolutions combined with upsampling are to segment different tumor areas. For a fine classification of pixel-wise precision, the feature maps are modulated by adding the weighted local feature maps generated in the first half of the CNN. The system has only 11716 parameters to be trained and, for a patient case of (240x240x155 x3) voxels, it requires only 21.14G Flops to complete the test. Hence, it is likely the simplest CNN system, so far reported, for brain tumor segmentation. The performance of the proposed system has been evaluated by means of CBICA Image Processing Portal with samples from dataset BRATS2018. Requiring a very low computation volume, the proposed system delivers a high segmentation quality indicated by its average Dice scores of 0.75, 0.88 and 0.76 for enhancing tumor, whole tumor and tumor core, respectively, and the median Dice scores of 0.85, 0.92, and 0.86. Its processing quality is comparable to the best ones so far reported. The consistency in system performance has also been measured, and the results have demonstrated that the system is able to reproduce almost the same output to the same input after retraining. In conclusion, the proposed CNN system has been designed to meet the specific needs to segment brain tumors or other kinds of tumors in medical images. In this way, the redundancy in computation can be minimized, the information density in data flow increased, and the computation efficiency/quality improved. This design demonstrates that a CNN system can be made to perform a high-quality processing, at a very low computation cost, for a specific application. Hence, ASCNN is an effective approach to lower the barrier of computation resource requirement of CNN systems in order to make them more implementable and applicable for general public