Machine learning methods for histopathological image analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

Extensive spontaneous plasticity of corticospinal projections after primate spinal cord injury.

Although axonal regeneration after CNS injury is limited, partial injury is frequently accompanied by extensive functional recovery. To investigate mechanisms underlying spontaneous recovery after incomplete spinal cord injury, we administered C7 spinal cord hemisections to adult rhesus monkeys and analyzed behavioral, electrophysiological and anatomical adaptations. We found marked spontaneous plasticity of corticospinal projections, with reconstitution of fully 60% of pre-lesion axon density arising from sprouting of spinal cord midline-crossing axons. This extensive anatomical recovery was associated with improvement in coordinated muscle recruitment, hand function and locomotion. These findings identify what may be the most extensive natural recovery of mammalian axonal projections after nervous system injury observed to date, highlighting an important role for primate models in translational disease research

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows

Purpose: Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Methods: Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. Results: In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user's side and on the algorithmic side. Conclusions: The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the syste

Learning regions of interest from low level maps in virtual microscopy

Virtual microscopy can improve the workflow of modern pathology laboratories, a goal limited by the large size of the virtual slides (VS). Lately, determination of the Regions of Interest has shown to be useful in navigation and compression tasks. This work presents a novel method for establishing RoIs in VS, based on a relevance score calculated from example images selected by pathologist. The process starts by splitting the Virtual Slide (VS) into a grid of blocks, each represented by a set of low level features which aim to capture the very basic visual properties, namely, color, intensity, orientation and texture. The expert selects then two blocks i.e. A typical relevant (irrelevant) instance. Different similarity (disimilarity) maps are then constructed, using these positive (negative) examples. The obtained maps are then integrated by a normalization process that promotes maps with a similarity global maxima that largely exceeds the average local maxima. Each image region is thus entailed with an associated score, established by the number of closest positive (negative) blocks, whereby any block has also an associated score. Evaluation was carried out using 8 VS from different tissues, upon which a group of three pathologists had navigated. Precision-recall measurements were calculated at each step of any actual navigation, obtaining an average precision of 55% and a recall of about 38% when using the available set of navigations