Skin melanoma represents a major health issue. Today, diagnosis and follow-up can rely on computer-aided diagnosis tools, to help dermatologists segment and quantitatively describe the image content. In particular, deep convolutional neural networks (CNN) have lately been become the state-of-the-art in automated medical image segmentation. The loss function plays an important role in CNN in the backpropagation process. In this work, we propose a metric-inspired loss function, based on the Kappa index. Unlike the Dice loss, a standard loss used in image segmentation CNN, the Kappa loss takes into account all the pixels in the image, including the true negative - we believe this can improve the accuracy of the evaluation process between prediction and ground truth. We demonstrate the differentiability of the Kappa loss and present some results on six public datasets of skin lesion. Experiments have shown promising results in skin lesion segmentation.

Kappa Loss for Skin Lesion Segmentation in Fully Convolutional Network

Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke.

Stroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. We developed a machine learning model that used the apparent diffusion coefficient and diffusion weighted imaging series. It was trained on 6,657 MRI studies from Massachusetts General Hospital (MGH; Boston, USA). All studies were labelled positive or negative for infarct (classification annotation) with 377 having the region of interest outlined (segmentation annotation). The different annotation types facilitated training on more studies while not requiring the extensive time to manually segment every study. We initially validated the model on studies sequestered from the training set. We then tested the model on studies from three clinical scenarios: consecutive stroke team activations for 6-months at MGH, consecutive stroke team activations for 6-months at a hospital that did not provide training data (Brigham and Women's Hospital [BWH]; Boston, USA), and an international site (Diagnósticos da América SA [DASA]; Brazil). The model results were compared to radiologist ground truth interpretations. The model performed better when trained on classification and segmentation annotations (area under the receiver operating curve [AUROC] 0.995 [95% CI 0.992-0.998] and median Dice coefficient for segmentation overlap of 0.797 [IQR 0.642-0.861]) compared to segmentation annotations alone (AUROC 0.982 [95% CI 0.972-0.990] and Dice coefficient 0.776 [IQR 0.584-0.857]). The model accurately identified infarcts for MGH stroke team activations (AUROC 0.964 [95% CI 0.943-0.982], 381 studies), BWH stroke team activations (AUROC 0.981 [95% CI 0.966-0.993], 247 studies), and at DASA (AUROC 0.998 [95% CI 0.993-1.000], 171 studies). The model accurately segmented infarcts with Pearson correlation comparing model output and ground truth volumes between 0.968 and 0.986 for the three scenarios. Acute infarct can be accurately detected and segmented on MRI in real-world clinical scenarios using a machine learning model. 

/pdf/development-and-clinical-application-of-a-deep-learning-2ne1xpdg.pdf

Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging

Cerebrovascular accident, or commonly known as stroke, is an acute disease with extreme impact on patients and healthcare systems and is the second largest cause of death worldwide. Fast and precise stroke lesion detection and location is an extreme important process with regards to stroke diagnosis, treatment, and prognosis. Except from the manual segmentation approach, machine learning based segmentation methods are the most promising ones when considering efficiency and accuracy, and convolutional neural network based models are the first of its kind. However, most of these neural network models do not really align with the brain anatomical structures. Intuitively, this work presents a more brain alike model which mimics the anatomical structure of the human visual cortex. Through the preliminary experiments on the stroke lesion segmentation task, the proposed model is found to be able to perform equally well or better to the de-facto standard U-Net. Part of the implementation will be made available at this https URL.

Stroke Lesion Segmentation with Visual Cortex Anatomy Alike Neural Nets.

Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.

/pdf/mr-image-based-attenuation-correction-of-brain-pet-imaging-17bmm2hal2.pdf

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

Improved outcome in patients with ischemic stroke is achieved through acute diagnosis and early restoration of cerebral flow in appropriate patients Diffusion-weighted MR imaging (DWI) plays a central role in these efforts by enabling rapid early localization and quantification of ischemic lesions Automated detection and quantification can potentially accelerate diagnosis, improve treatment safety and efficacy and reduce costs However, the manual quantification of acute ischemic stroke volumes for algorithm training is time consuming and imprecise We present YNet as a novel fully-automated deep learning algorithm for detection and volumetric segmentation and quantification of acute cerebral ischemic lesions from DWI The algorithm is a semi-supervised multi-tasking deep neural network architecture we developed that enables the combination of both weak labels derived from radiology report classification and manually delineated pixel level training data The model is trained on a very large dataset of 10000 studies, achieves detection sensitivity 0981, detection specificity 0980 and segmentation Dice score 0623 on a heterogeneous test set

Detection and Delineation of Acute Cerebral Infarct on DWI Using Weakly Supervised Machine Learning

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Detection and Delineation of Acute Cerebral Infarct on DWI Using Weakly Supervised Machine Learning