A deep learning approach to predict collateral flow in stroke patients using radiomic features from perfusion images

What fucking stupidity! The research should have been; 'How to create cerebral blood flow and better oxygenation in patients to save neurons from dying!' You're all fired. 

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  The latest useless shit here:

A deep learning approach to predict collateral flow in stroke patients using radiomic features from perfusion images

Giles Tetteh^1,2*, Fernando Navarro¹, Raphael Meier³, Johannes Kaesmacher³, Johannes C. Paetzold^1†, Jan S. Kirschke², Claus Zimmer², Roland Wiest³ and Bjoern H. Menze^1,4

• ¹Department of Computer Science, Technische Universität München, München, Germany
• ²Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, München, Germany
• ³Institute for Diagnostic and Interventional Neuroradiology, Inselspital University Hospital, Bern, Switzerland
• ⁴Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland

Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by arterial obstruction. The quality of collateral circulation has been established as a key factor in determining the likelihood of a favorable clinical outcome and goes a long way to determining the choice of a stroke care model. Though many imaging and grading methods exist for quantifying collateral blood flow, the actual grading is mostly done through manual inspection. This approach is associated with a number of challenges. First, it is time-consuming. Second, there is a high tendency for bias and inconsistency in the final grade assigned to a patient depending on the experience level of the clinician. We present a multi-stage deep learning approach to predict collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data. First, we formulate a region of interest detection task as a reinforcement learning problem and train a deep learning network to automatically detect the occluded region within the 3D MR perfusion volumes. Second, we extract radiomic features from the obtained region of interest through local image descriptors and denoising auto-encoders. Finally, we apply a convolutional neural network and other machine learning classifiers to the extracted radiomic features to automatically predict the collateral flow grading of the given patient volume as one of three severity classes - no flow (0), moderate flow (1), and good flow (2). Results from our experiments show an overall accuracy of 72% in the three-class prediction task. With an inter-observer agreement of 16% and a maximum intra-observer agreement of 74% in a similar experiment, our automated deep learning approach demonstrates a performance comparable to expert grading, is faster than visual inspection, and eliminates the problem of grading bias.

1. Introduction

Collateral circulation results from specialized anastomotic channels which are present in most tissues and capable of providing nutrient perfusion to regions with compromised blood flow due to ischemic injuries caused by ischemic stroke, coronary atherosclerosis, peripheral artery disease, and similar conditions or diseases (1). Collateral circulation helps to sustain blood flow in the ischaemic areas in acute, subacute, or chronic phases after an ischaemic stroke or transient ischaemic attack (2). The quality of collateral circulation has been convincingly established as a key factor in determining the likelihood of successful reperfusion and favorable clinical outcome (3). It is also seen as one of the major determinants of infarct growth in the early time windows which is likely to have an impact on the chosen stroke care model that is the decision to transport or treat eligible patients immediately.

A high number of imaging methods exist to assess the structure of the cerebral collateral circulation and several grading criteria have been proposed to quantify the characteristics of collateral blood flow. However, this grading is mostly done through visual inspection of the acquired images which introduces two main challenges.

First, there are biases and inconsistencies in the current grading approaches: There is a high tendency of introducing bias in the final grade assigned to a patient depending on the experience level of the clinician. There are inconsistencies also in the grade assigned by a particular clinician at different times for the same patient. These inconsistencies are quantified at 16% interobserver agreement and a maximum intraobserver agreement of 74% respectively in a similar study by Ben Hassen et al. (4).

Second, grading is time-consuming and tedious: Aside the problem of bias prediction, it also takes the clinician several minutes to go through the patient images to first select the correct image sequence, detect the region of collateral flow and then to be able to assign a grading a period of time which otherwise could have been invested in the treatment of the patient.

In this work, we analyze several machine learning and deep learning strategies that aim toward automating the process of collateral circulation grading. We present a set of solutions focusing on two main aspects of the task at hand.

First, the region of interest needs to be identified. We automate the extraction of the region of interest (ROI) from the patient images using deep reinforcement learning (RL). This is necessary for achieving a fully automated system that will require no human interaction and save the clinician the time spent on performing this task.

Finally, the region of interest needs to be processed and classified. We consider various feature extraction schemes and classifiers suitable for the task described above. This helps to extract useful image features, both learned and hand-crafted, which are relevant to the classification task. We predict digitally subtracted angiography (DSA) based collateral flow grading from MR perfusion images in this task. This saves the time required in choosing the right DSA sequence from the multiple DSA sequences acquired and helps achieve a fully automated system.