blob loss: instance imbalance aware loss functions for semantic segmentation

Deep convolutional neural networks have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Sorensen Dice coefficient. By design, DSC can tackle class imbalance; however, it does not recognize instance imbalance within a class. As a result, a large foreground instance can dominate minor instances and still produce a satisfactory Sorensen Dice coefficient. Nevertheless, missing out on instances will lead to poor detection performance. This represents a critical issue in applications such as disease progression monitoring. For example, it is imperative to locate and surveil small-scale lesions in the follow-up of multiple sclerosis patients. We propose a novel family of loss functions, nicknamed blob loss, primarily aimed at maximizing instance-level detection metrics, such as F1 score and sensitivity. Blob loss is designed for semantic segmentation problems in which the instances are the connected components within a class. We extensively evaluate a DSC-based blob loss in five complex 3D semantic segmentation tasks featuring pronounced instance heterogeneity in terms of texture and morphology. Compared to soft Dice loss, we achieve 5 percent improvement for MS lesions, 3 percent improvement for liver tumor, and an average 2 percent improvement for Microscopy segmentation tasks considering F1 score

Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings

Human ratings are abstract representations of segmentation quality. To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models. We evaluate on a complex multi-class segmentation problem, specifically glioma segmentation following the BraTS annotation protocol. The training data features quality ratings from 15 expert neuroradiologists on a scale ranging from 1 to 6 stars for various computer-generated and manual 3D annotations. Even though the networks operate on 2D images and with scarce training data, we can approximate segmentation quality within a margin of error comparable to human intra-rater reliability. Segmentation quality prediction has broad applications. While an understanding of segmentation quality is imperative for successful clinical translation of automatic segmentation quality algorithms, it can play an essential role in training new segmentation models. Due to the split-second inference times, it can be directly applied within a loss function or as a fully-automatic dataset curation mechanism in a federated learning setting

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the \textit{VAscular Lesions DetectiOn and Segmentation} (\textit{Where is VALDO?}) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level

ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset

Magnetic resonance imaging (MRI) is a central modality for stroke imaging. It is used upon patient admission to make treatment decisions such as selecting patients for intravenous thrombolysis or endovascular therapy. MRI is later used in the duration of hospital stay to predict outcome by visualizing infarct core size and location. Furthermore, it may be used to characterize stroke etiology, e.g. differentiation between (cardio)-embolic and non-embolic stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. Previous iterations of the Ischemic Stroke Lesion Segmentation (ISLES) challenge have aided in the generation of identifying benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions. This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n=250 and a test dataset of n=150. All training data will be made publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge with the goal of finding algorithmic methods to enable the development and benchmarking of robust and accurate segmentation algorithms for ischemic stroke

Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis

Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object's reflectance allows fingerprinting its physical, biochemical, and physiological properties. HSI has been applied for various applications, such as remote sensing and biological tissue analysis. Recently, HSI was also used to differentiate between healthy and pathological tissue under operative conditions in a surgery room on patients diagnosed with brain tumors. In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores. By using the principal component analysis (PCA), we determine the most relevant spectral features for intra- and inter-tissue class differentiation. Furthermore, we demonstrate that such spectral features are correlated with the spectra of cytochrome, i.e., the chromophore highly involved in (hyper) metabolic processes. Identifying such fingerprints of chromophores in reflectance spectra is a key step for automated molecular profiling and, eventually, expert-free biomarker discovery

The p53 binding protein PDCD5 is not rate-limiting in DNA damage induced cell death

The tumour suppressor p53 is an important mediator of cell cycle arrest and apoptosis in response to DNA damage, acting mainly by transcriptional regulation of specific target genes. The exact details how p53 modulates this decision on a molecular basis is still incompletely understood. One mechanism of regulation is acetylation of p53 on lysine K120 by the histone-acetyltransferase Tip60, resulting in preferential transcription of proapoptotic target genes. PDCD5, a protein with reported pro-apoptotic function, has recently been identified as regulator of Tip60-dependent p53-acetylation. In an effort to clarify the role of PDCD5 upon DNA damage, we generated cell lines in which PDCD5 expression was conditionally ablated by shRNAs and investigated their response to genotoxic stress. Surprisingly, we failed to note a rate-limiting role of PDCD5 in the DNA damage response. PDCD5 was dispensable for DNA damage induced apoptosis and cell cycle arrest and we observed no significant changes in p53 target gene transcription. While we were able to confirm interaction of PDCD5 with p53, we failed to do so for Tip60. Altogether, our results suggest a role of PDCD5 in the regulation of p53 function but unrelated to cell cycle arrest or apoptosis, at least in the cell types investigated.FP06 RTN ‘ApopTrain’Tyrolean Science FundKrebshilfe-Tyro

A magnet attached to the forehead disrupts magnetic compass orientation in a migratory songbird

For studies on magnetic compass orientation and navigation performance in small bird species, controlled experiments with orientation cages inside an electromagnetic coil system are the most prominent methodological paradigm. These are, however, not applicable when studying larger bird species and/or orientation behaviour during free flight. For this, researchers have followed a very different approach. By attaching small magnets to birds, they intended to deprive them of access to meaningful magnetic information. Unfortunately, results from studies using this approach appear rather inconsistent. As these are based on experiments with birds under free flight conditions, which usually do not allow exclusion of other potential orientation cues, an assessment of the overall efficacy of this approach is difficult to conduct. Here, we directly test the efficacy of small magnets for temporarily disrupting magnetic compass orientation in small migratory songbirds using orientation cages under controlled experimental conditions. We found that birds which have access to the Earth’s magnetic field as their sole orientation cue show a general orientation towards their seasonally appropriate migratory direction. When carrying magnets on their forehead under these conditions, the same birds become disoriented. However, under changed conditions that allow birds access to other (i.e. celestial) orientation cues, any disruptive effect of the magnets they carry appears obscured. Our results provide clear evidence for the efficacy of the magnet approach for temporarily disrupting magnetic compass orientation in birds, but also reveal its limitations for application in experiments under free flight conditions

Primitive Simultaneous Optimization of Similarity Metrics for Image Registration

Even though simultaneous optimization of similarity metrics represents a standard procedure in the field of semantic segmentation, surprisingly, this does not hold true for image registration. To close this unexpected gap in the literature, we investigate in a complex multi-modal 3D setting whether simultaneous optimization of registration metrics, here implemented by means of primitive summation, can benefit image registration. We evaluate two challenging datasets containing collections of pre- to post-operative and pre- to intra-operative Magnetic Resonance Imaging (MRI) of glioma. Employing the proposed optimization we demonstrate improved registration accuracy in terms of Target Registration Error (TRE) on expert neuroradiologists' landmark annotations