Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes

Taha, Ahmed; Lo, Pechin; Li, Junning; Zhao, Tao

doi:10.1007/978-3-030-00937-3_53

Cited by 53 publications

(43 citation statements)

References 15 publications

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“…The model is trained on clinical Computed Tomography (CT) data, and it is shown to perform automated multi-organ segmentation of abdominal CT with 90% average Dice score across all targeted organs. A CNN method, termed Kid-Net, is proposed for kidney vessels; artery, vein and collecting system (ureter) segmentation by Taha et al [147]. Their model is trained in 5. https://www.creatis.insa-lyon.fr/Challenge/acdc/ an end-to-end fashion using 3D CT-volume patches.…”

Section: Abdomenmentioning

confidence: 99%

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

et al. 2019

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Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in 'Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out 'lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.

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Section: Abdomenmentioning

confidence: 99%

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

et al. 2019

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“…Right now, surgery is the most common treatment option. Semantic segmentation of the kidneys and the tumorous tissue is a promising first step towards improving the treatment outcome, for example by serving as processing step in surgery planning [16], or by enabling research that attempts to relate tumor morphology to surgical outcome [3,8].…”

Section: Introductionmentioning

confidence: 99%

An attempt at beating the 3D U-Net

Isensee¹,

Maier‐Hein²

2019

Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19

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The U-Net is arguably the most successful segmentation architecture in the medical domain. Here we apply a 3D U-Net to the 2019 Kidney and Kidney Tumor Segmentation Challenge and attempt to improve upon it by augmenting it with residual and pre-activation residual blocks. Cross-validation results on the training cases suggest only very minor, barely measurable improvements. Due to marginally higher dice scores, the residual 3D U-Net is chosen for test set prediction. With a Composite Dice score of 91.23 on the test set, our method outperformed all 105 competing teams and won the KiTS2019 challenge by a small margin.

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“…We were inspired by the anatomic structure of the pulmonary artery, automatically tracked the directions of artery which starts from pulmonary trunk. There are no interactive options in the method, the degree of automation is comparative to machine learning [8,9].…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of arterial tree from 3D computed tomographic pulmonary angiography (CTPA) scans

Zhang

Sun

Wei

et al. 2019

Computer Assisted Surgery

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Pulmonary embolism (PE) and other pulmonary vascular diseases, have been found associated with the changes in arterial morphology. To detect arterial changes, we propose a novel, fully automatic method that can extract pulmonary arterial tree in computed tomographic pulmonary angiography (CTPA) images. The approach is based on the fuzzy connectedness framework, combined with 3D vessel enhancement and Harris Corner detection to achieve accurate segmentation. The effectiveness and robustness of the method is validated in clinical datasets consisting of 10 CT angiography scans (6 without PE and 4 with PE). The performance of our method is compared with manual classification and machine learning method based on random forest. Our method achieves a mean accuracy of 92% when compared to manual reference, which is higher than the 89% accuracy achieved by machine learning. This performance of the segmentation for pulmonary arteries may provide a basis for the CAD application of PE. KEYWORDSPulmonary artery segmentation; 3D vessel enhancement; fuzzy connectedness; pulmonary embolism

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Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes

Cited by 53 publications

References 15 publications

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

An attempt at beating the 3D U-Net

Automatic segmentation of arterial tree from 3D computed tomographic pulmonary angiography (CTPA) scans

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