Deep-learning-based image segmentation for uncommon ischemic stroke:From infants to adults

Developing deep learning-based algorithms that accurately segment structures in scans that are relevant to treatment or evaluation of the outcome of uncommon stroke is a difficult task. The difficulty is due to the presence of image artefacts, few data being available to train the networks, and the small volume of some of the target structures. Hence, the aim of this thesis was to investigate, develop, and evaluate deep learning-based algorithms for automatic segmentation of images of uncommon sub-types of stroke. In chapter two, transfer learning strategies for automated medical image segmentation were evaluated. Our results showed that pre-training on a segmentation task on the same domain as the target segmentation task yielded the greatest improvement in spatial agreement. However, our results have also shown that the choice of source task and domain have an inconsistent effect on the detection rate.In chapters three and four, segmentation algorithms for scans of patients suffering from posterior circulation stroke were developed. In chapter three, deep transfer learning was used to improve segmentation of lesions caused by posterior circulation stroke. In chapter four an algorithm, which restricted inference to the area surrounding the brain stem, was developed to segment thrombi in the posterior circulation. In chapter five, two instances of an algorithm were developed to segment brain tissue types and the ischemic lesion per hemisphere in patients suffering from perinatal arterial ischemic stroke. One instance segmented scans acquired at term, the other instance segmented scans acquired at follow-up

Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning

Final lesion volume (FLV) is a surrogate outcome measure in anterior circulation stroke (ACS). In posterior circulation stroke (PCS), this relation is plausibly understudied due to a lack of methods that automatically quantify FLV. The applicability of deep learning approaches to PCS is limited due to its lower incidence compared to ACS. We evaluated strategies to develop a convolutional neural network (CNN) for PCS lesion segmentation by using image data from both ACS and PCS patients. We included follow-up non-contrast computed tomography scans of 1018 patients with ACS and 107 patients with PCS. To assess whether an ACS lesion segmentation generalizes to PCS, a CNN was trained on ACS data (ACS-CNN). Second, to evaluate the performance of only including PCS patients, a CNN was trained on PCS data. Third, to evaluate the performance when combining the datasets, a CNN was trained on both datasets. Finally, to evaluate the performance of transfer learning, the ACS-CNN was fine-tuned using PCS patients. The transfer learning strategy outperformed the other strategies in volume agreement with an intra-class correlation of 0.88 (95% CI: 0.83–0.92) vs. 0.55 to 0.83 and a lesion detection rate of 87% vs. 41–77 for the other strategies. Hence, transfer learning improved the FLV quantification and detection rate of PCS lesions compared to the other strategies

Deep-learning-based image segmentation for uncommon ischemic stroke: From infants to adults

Developing deep learning-based algorithms that accurately segment structures in scans that are relevant to treatment or evaluation of the outcome of uncommon stroke is a difficult task. The difficulty is due to the presence of image artefacts, few data being available to train the networks, and the small volume of some of the target structures. Hence, the aim of this thesis was to investigate, develop, and evaluate deep learning-based algorithms for automatic segmentation of images of uncommon sub-types of stroke. In chapter two, transfer learning strategies for automated medical image segmentation were evaluated. Our results showed that pre-training on a segmentation task on the same domain as the target segmentation task yielded the greatest improvement in spatial agreement. However, our results have also shown that the choice of source task and domain have an inconsistent effect on the detection rate. In chapters three and four, segmentation algorithms for scans of patients suffering from posterior circulation stroke were developed. In chapter three, deep transfer learning was used to improve segmentation of lesions caused by posterior circulation stroke. In chapter four an algorithm, which restricted inference to the area surrounding the brain stem, was developed to segment thrombi in the posterior circulation. In chapter five, two instances of an algorithm were developed to segment brain tissue types and the ischemic lesion per hemisphere in patients suffering from perinatal arterial ischemic stroke. One instance segmented scans acquired at term, the other instance segmented scans acquired at follow-up

Tatabahasa Indonesia untuk sekolah landjutan atas : bentuk kata I

135 p.; 35 cm

Domain- and Task-Specific Transfer Learning For Medical Segmentation Tasks

Background and objectives: Transfer learning is a valuable approach to perform medical image segmentation in settings with limited cases available for training convolutional neural networks (CNN). Both the source task and the source domain influence transfer learning performance on a given target medical image segmentation task. This study aims to assess transfer learning-based medical segmentation task performance for various source task and domain combinations. Methods: CNNs were pre-trained on classification, segmentation, and self-supervised tasks on two domains: natural images and T1 brain MRI. Next, these CNNs were fine-tuned on three target T1 brain MRI segmentation tasks: stroke lesion, MS lesions, and brain anatomy segmentation. In all experiments, the CNN architecture and transfer learning strategy were the same. The segmentation accuracy on all target tasks was evaluated using the mIOU or Dice coefficients. The detection accuracy was evaluated for the stroke and MS lesion target tasks only. Results: CNNs pre-trained on a segmentation task on the same domain as the target tasks resulted in higher or similar segmentation accuracy compared to other source task and domain combinations. Pre-training a CNN on ImageNet resulted in a comparable, but not consistently higher lesion detection rate, despite the amount of training data used being 10 times larger. Conclusions: This study suggests that optimal transfer learning for medical segmentation is achieved with a similar task and domain for pre-training. As a result, CNNs can be effectively pre-trained on smaller datasets by selecting a source domain and task similar to the target domain and task