Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbackComment: 16 page

Production, purification and characterization of a 50-kDa extracellular metalloprotease from Serratia marcescens

The extracellular metalloprotease (SMP 6.1) produced by a soil isolate of Serratia marcescens NRRL B-23112 was purified and characterized. SMP 6.1 was purified from the culture supernatant by ammonium sulfate precipitation, acetone fractional precipitation, and preparative isoelectric focusing. SMP 6.1 has a molecular mass of approximately 50 900 Da by sodium dodecyl sulfate/polyacrylamide gel electrophoresis (SDS-PAGE). The following substrates were hydrolyzed: casein, bovine serum albumin, and hide powder. SMP 6.1 has the characteristics of a metalloprotease, a pH optimum of 10.0, and a temperature optimum of 42°C. The isoelectric point of the protease is 6.1. Restoration of proteolytic activity by in-gel renaturation after SDS-PAGE indicates a single poly,peptide chain. SMP 6.1 is inhibited by EDTA (9 μg/ml) and not inhibited by antipain dihydrochloride (120 μg/ml), aprotinin (4 μg/ml), bestatin (80 μg/ml), chymostatin (50 μg/ml), E-64 (20 μg/ml), leupeptin (4 μg/ml), Pefabloc SC (2000 μg/ml), pepstatin (4 μg/ml), phosphoramidon (660 μg/ml), or phenylmethylsulfonyl fluoride (400 μg/ml). SMP 6.1 retains full activity in the presence of SDS (1% w/v), Tween-20 (1% w/v), Triton X-100 (1% w/v), ethanol (5% v/v), and 2-mercaptoethanol (0.5% v/v). The extracellular metalloprotease SMP 6.1 differs from the serratiopeptidase (Sigma) produced by S. marcescens ATCC 27117 in the following characteristics: isoelectric point, peptide mapping and nematolytic properties