Solution to the Kidney Tumor Segmentation Challenge 2019

Kidney and kidney tumor segmentation are essential steps in kidney cancer surgery. In this paper, we focus on addressing hard cases and exploring the kidney tumor shape prior rather than developing new convolution neural network architectures. Specifically, we train additional tumor segmentation networks to bias the ensemble classifier to tumor. Moreover, we propose the compact loss function to constrain the shape of the tumor segmentation results. Experiments on KiTS challenge show that both hard mining and compact can improve the performance of U-Net baseline

A Two-stage Method with a Shared 3D U-Net for Left Atrial Segmentation of Late Gadolinium-Enhanced MRI Images

Objective: This study was aimed at validating the accuracy of a proposed algorithm for fully automatic 3D left atrial segmentation and to compare its performance with existing deep learning algorithms. Methods: A two-stage method with a shared 3D U-Net was proposed to segment the 3D left atrium. In this architecture, the 3D U-Net was used to extract 3D features, a two-stage strategy was used to decrease segmentation error caused by the class imbalance problem, and the shared network was designed to decrease model complexity. Model performance was evaluated with the DICE score, Jaccard index and Hausdorff distance. Results: Algorithm development and evaluation were performed with a set of 100 late gadolinium-enhanced cardiovascular magnetic resonance images. Our method achieved a DICE score of 0.918, a Jaccard index of 0.848 and a Hausdorff distance of 1.211, thus, outperforming existing deep learning algorithms. The best performance of the proposed model (DICE: 0.851; Jaccard: 0.750; Hausdorff distance: 4.382) was also achieved on a publicly available 2013 image data set. Conclusion: The proposed two-stage method with a shared 3D U-Net is an efficient algorithm for fully automatic 3D left atrial segmentation. This study provides a solution for processing large datasets in resource-constrained applications. Significance Statement: Studying atrial structure directly is crucial for comprehending and managing atrial fibrillation (AF). Accurate reconstruction and measurement of atrial geometry for clinical purposes remains challenging, despite potential improvements in the visibility of AF-associated structures with late gadolinium-enhanced magnetic resonance imaging. This difficulty arises from the varying intensities caused by increased tissue enhancement and artifacts, as well as variability in image quality. Therefore, an efficient algorithm for fully automatic 3D left atrial segmentation is proposed in the present study

Evaluation with an Independent Dataset of a Deep Learning-based Left Atrium Segmentation Method

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Director/s: Gaspar Delso i Roser Sala. Tutor: Manel PuigAtrial fibrillation (AF) is the most prevalent type of arrhythmia nowadays. Even though it is associated with significant morbidity and mortality, there is still a substantial lack of basic understanding of the left atrium (LA) and pulmonary veins (PVs) anatomical structure that curbs the performance of current clinical treatments for the disease. Thus, segmentation and 3D reconstruction of the LA and PVs are of crucial importance for the diagnosis and treatment of AF. In this context, cardiac 3D Late Gadolinium Magnetic Resonance Imaging (LGE-MRI) appear as a very good tool for cardiac tissue characterization and myocardial fibrosis detection. In fact, these images have been proofed as reliable predictors of catheter ablation success, which is often the chosen treatment for AF patients. Several manual and semi-automatic segmentation tools from LGE-MRI scans are currently in use, but these are very time-consuming and highly prone to errors, hence the need for an automatic segmentation approach. With the rise of deep learning and convolutional neural networks, a number of automatic schemes are being developed. In this project, we evaluate a model that has been developed at the Hospital Clínic de Barcelona for obtaining an automatic segmentation of the LA using a deep learning architecture. Concretely, we tested this model with an independent set of images from another MRI vendor, and we obtained a set of quantitative and qualitative measures to validate the results. For the pursuit of our aims, this work begins with the state-of-the-art for LA segmentation of LGEMRI scans and with a market analysis of the field. We then present our proposed solution together with the obtained results and the corresponding conclusions