scholarly journals Knowledge‐based and deep learning‐based automated chest wall segmentation in magnetic resonance images of extremely dense breasts

2019 ◽  
Vol 46 (10) ◽  
pp. 4405-4416 ◽  
Author(s):  
Erik Verburg ◽  
Jelmer M. Wolterink ◽  
Stephanie N. Waard ◽  
Ivana Išgum ◽  
Carla H. Gils ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Chest Wall ◽  
Magnetic Resonance Images ◽  
Dense Breasts ◽  
Knowledge Based

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

scholarly journals Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Aniket A. Tolpadi ◽  
Jinhee J. Lee ◽  
Valentina Pedoia ◽  
Sharmila Majumdar
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Total Knee Replacement ◽  
Knee Replacement ◽  
Magnetic Resonance Images ◽  
Total Knee

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

2019 ◽  
Vol 19 (S9) ◽  
Author(s):  
Vitoantonio Bevilacqua ◽  
Antonio Brunetti ◽  
Giacomo Donato Cascarano ◽  
Andrea Guerriero ◽  
Francesco Pesce ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Kidney Disease ◽  
Autosomal Dominant ◽  
Automatic Segmentation ◽  
Semantic Segmentation ◽  
Magnetic Resonance Images ◽  
Polycystic Kidney ◽  
Autosomal Dominant Polycystic Kidney ◽  
Segmentation Of Images

Abstract Background The 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. Methods Two 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. Results Results 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. Conclusion The 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.

scholarly journals Deep learning-based quantification of abdominal fat on magnetic resonance images

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0204071 ◽  
Author(s):  
Andrew T. Grainger ◽  
Nicholas J. Tustison ◽  
Kun Qing ◽  
Rene Roy ◽  
Stuart S. Berr ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Abdominal Fat ◽  
Magnetic Resonance Images
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