14 research outputs found
Anatomical Data Augmentation For CNN based Pixel-wise Classification
In this work we propose a method for anatomical data augmentation that is
based on using slices of computed tomography (CT) examinations that are
adjacent to labeled slices as another resource of labeled data for training the
network. The extended labeled data is used to train a U-net network for a
pixel-wise classification into different hepatic lesions and normal liver
tissues. Our dataset contains CT examinations from 140 patients with 333 CT
images annotated by an expert radiologist. We tested our approach and compared
it to the conventional training process. Results indicate superiority of our
method. Using the anatomical data augmentation we achieved an improvement of 3%
in the success rate, 5% in the classification accuracy, and 4% in Dice.Comment: To be presented at IEEE ISBI 201
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094
Magnetic resonance enterography in pregnant women with Crohn’s disease: case series and literature review
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor
Segmentation Benchmark (LITS) organized in conjunction with the IEEE
International Symposium on Biomedical Imaging (ISBI) 2016 and International
Conference On Medical Image Computing Computer Assisted Intervention (MICCAI)
2017. Twenty four valid state-of-the-art liver and liver tumor segmentation
algorithms were applied to a set of 131 computed tomography (CT) volumes with
different types of tumor contrast levels (hyper-/hypo-intense), abnormalities
in tissues (metastasectomie) size and varying amount of lesions. The submitted
algorithms have been tested on 70 undisclosed volumes. The dataset is created
in collaboration with seven hospitals and research institutions and manually
reviewed by independent three radiologists. We found that not a single
algorithm performed best for liver and tumors. The best liver segmentation
algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation
the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image
data and manual annotations continue to be publicly available through an online
evaluation system as an ongoing benchmarking resource.Comment: conferenc
A review of magnetic resonance enterography-based indices for quantification of Crohn’s disease inflammation
Magnetic resonance enterography (MRE) is a leading radiological modality in Crohn’s disease (CD) and is used together with laboratory findings and endoscopic examinations for the evaluation of patients during initial diagnosis and follow up. Over the years, there has been great progress in the understanding of CD and there is a continuous strive to achieve better monitoring of patients and to develop new modalities which will predict disease course and thus help in clinical decisions making. An objective evaluation of CD using a quantification score is not a new concept and there are different clinical, endoscopies, radiological and combined indices which are used in clinical practice. Such scores are a necessity in clinical trials on CD for evaluation of disease response, however, there is no consensus of the preferred MRE score and they are not routinely used. This review presents MRE-based indices in use in the last decade: the Magnetic Resonance Index of Activity (MaRIA), the Clermont score, the Crohn’s Disease Magnetic Resonance Imaging (MRI) Index (CDMI), the Magnetic Resonance Enterography Global Score (MEGS) and the Lemann index. We compare the different indices and evaluate the clinical research that utilized them. The aim of this review is to provide a reference guide for researchers and clinicians who incorporate MRE indices in their work. When devising future indices, accumulated data of the existing indices must be taken into account, as each of the current indices has its own strengths and weakness