A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images

Abstract

BackgroundThe automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images.MethodsTwo different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted.ResultsResults show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach.ConclusionThe obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

Segmentation and Identification of Vertebrae in CT Scans Using CNN, k-Means Clustering and k-NN

“…For major details, surveys about U-shaped architectures and semantic segmentation approaches can be considered [25,26]. Applications of DL and semantic segmentation in the medical imaging include glomeruli segmentation from kidney biopsies [27,28], autosomal dominant polycystic kidney disease segmentation from magnetic resonance images [29], liver and vessels segmentation from CT scans [30] among others. In order to ensure the reproducibility of the algorithms introduced in Sections 4.1 and 4.2, and the visualization tool presented in Section 4.3, the code has been made publicly available on GitHub (https://github.com/Nicolik/Segm_Ident_Vertebrae_ CNN_kmeans_knn, last accessed: 6 June 2021).…”

Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects

“…Then, a classification CNN is used to perform a semantic segmentation, here, a classifying voxels into kidney and non kidney. In this initial work only a relatively small training set set of 57 images from 4 patients were used for training and testing the method while in the follow up paper a larger cohort was used [135]. This work is licensed under a Creative Commons Attribution 4.0 License.…”

Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics

“…This task consists of the labeling of each pixel of an input image, but without recognizing different instances of objects, opposed to what is done in instance segmentation architectures, such as Mask R-CNN [23,27]. Examples of applications include robotics, medical image processing, and human-computer interaction [28][29][30][31].…”