| 1. |
- Banerjee, Subhashis, et al.
(författare)
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Deep Curriculum Learning for Follow-up MRI Registration in Glioblastoma
- 2023
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Ingår i: Medical Imaging 2023. - : SPIE -Society of Photo-Optical Instrumentation Engineers. - 9781510660335 - 9781510660342
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Konferensbidrag (refereegranskat)abstract
- This paper presents a weakly supervised deep convolutional neural network-based approach to perform voxel-level3D registration between subsequent follow-up MRI scans of the same patient. To handle the large deformation inthe surrounding brain tissues due to the tumor’s mass effect we proposed curriculum learning-based training forthe network. Weak supervision helps the network to concentrate more focus on the tumor region and resectioncavity through a saliency detection network. Qualitative and quantitative experimental results show the proposedregistration network outperformed two popular state-of-the-art methods.
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| 2. |
- Kahraman, Ali T., et al.
(författare)
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Automated detection, segmentation and measurement of major vessels and the trachea in CT pulmonary angiography
- 2023
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Mediastinal structure measurements are important for the radiologist's review of computed tomography pulmonary angiography (CTPA) examinations. In the reporting process, radiologists make measurements of diameters, volumes, and organ densities for image quality assessment and risk stratification. However, manual measurement of these features is time consuming. Here, we sought to develop a time-saving automated algorithm that can accurately detect, segment and measure mediastinal structures in routine clinical CTPA examinations. In this study, 700 CTPA examinations collected and annotated. Of these, a training set of 180 examinations were used to develop a fully automated deterministic algorithm. On the test set of 520 examinations, two radiologists validated the detection and segmentation performance quantitatively, and ground truth was annotated to validate the measurement performance. External validation was performed in 47 CTPAs from two independent datasets. The system had 86-100% detection and segmentation accuracy in the different tasks. The automatic measurements correlated well to those of the radiologist (Pearson's r 0.68-0.99). Taken together, the fully automated algorithm accurately detected, segmented, and measured mediastinal structures in routine CTPA examinations having an adequate representation of common artifacts and medical conditions.
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| 3. |
- Kundu, Swagata, et al.
(författare)
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3-D Attention-SEV-Net for Segmentation of Post-operative Glioblastoma with Interactive Correction of Over-Segmentation
- 2023
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Ingår i: Pattern Recognition and Machine Intelligence, PREMI 2023. - : Springer. - 9783031451690 - 9783031451706 ; , s. 380-387
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Konferensbidrag (refereegranskat)abstract
- Accurate localization and volumetric quantification of postoperative glioblastoma are of profound importance for clinical applications like post-surgery treatment planning, monitoring of tumor regrowth, and radiotherapy map planning. Manual delineation consumes more time and error prone thus automated 3-D quantification of brain tumors using deep learning algorithms from MRI scans has been used in recent years. The shortcoming with automated segmentation is that it often over-segments or under-segments the tumor regions. An interactive deep-learning tool will enable radiologists to correct the over-segmented and under-segmented voxels. In this paper, we proposed a network named Attention-SEV-Net which outperforms state-of-the-art network architectures. We also developed an interactive graphical user interface, where the initial 3-D segmentation of contrast-enhanced tumor can be interactively corrected to remove falsely detected isolated tumor regions. Attention-SEV-Net is trained with BraTS-2021 training data set and tested on Uppsala University post-operative glioblastoma dataset. The methodology outperformed state-of-the-art networks like U-Net, VNet, Attention U-Net and Residual U-Net. The mean dice score achieved is 0.6682 and the mean Hausdorff distance-95 got is 8.96mm for the Uppsala University dataset.
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