To realize the annotations, there are several possibilities on a technical level. Given our limited expertise in muscle anatomy and the challenge of accurately identifying muscular structures, we decided to switch from CSI annotations to SPLAT.

CSI (Contouring. Snapping. Interpolation) CSI is a macro module and a tool for interactive segmentation composed of sub-modules for contouring, snapping and interpolation. It implements a universal editor for creating and editing contours. The focus is on the use of free-form contours to enable flexible and precise contouring. Snapping aims to improve the accuracy of contours by automatically adjusting to object boundaries, resulting in more accurate segmentation. Finally, CSI includes interpolation functions to supplement irregularly sampled contour points. Interpolation thus contributes to the smoothing and continuity of contours, improving the overall performance of segmentation.

SPLAT (Sparse Label Annotation) Similar to CSI, SPLAT is a method for labeling image regions in medical image processing. In contrast to conventional annotations, which only provide binary information such as background and foreground, SPLAT enables a more differentiated labeling of image regions. With SPLAT, not only background and foreground can be labeled, but also additional categories such as undefined or uncertain areas can be marked. This enables a more precise characterization of image regions, especially in complex medical images where the assignment of pixels to specific structures or tissues can be difficult.

In computer science, neural networks are increasingly being used for image processing/analysis. In medical informatics in particular, they offer a great opportunity to analyze structures and patterns in imaging procedures such as MRI/CT and X-ray. Evaluationmetrics are a key feature for the development and improvement of those networks, thus we focused on research and implementation of the metrics in regards to our Sarcopenia project.

Various models are trained for tasks such as the registration and segmentation of organs or other tissue structures. The evaluation of a single model can be done with many known metrics - but these can have different meanings for different models that have been trained/optimized for different applications.

In the "Model Evaluation" subproject of DeepAnatomy, we took a closer look at different models - with a focus on muscle segmentation - and compared/evaluated them. As this year's focus was on pathological muscle atrophy (sarcopenia), we particularly examined the models' ability to segment muscles in the abdomen and thorax.

Graphical tools are of great importance for monitoring the performance of neural networks in a targeted manner. In the "Blossom" project, we set ourselves the goal of improving the efficiency and transparency of neural network training processes by repairing and subsequently revising a previously existing solution. The first part involved penetrating the existing cloud infrastructure in order to be able to reintegrate the repair of the original software.

The decision was then made to integrate new libraries in order to modernize and expand the visualization. A key approach is to provide real-time visualizations of the training lot and validation metrics throughout the training process. These visualizations make it possible to immediately identify patterns and trends in the training process and react accordingly. By monitoring training progress in real time, we can gain immediate insights into the performance of our model. This enables us to recognize problems such as overfitting, underfitting or gradient explosions at an early stage and address them in a targeted manner.

Other relevant aspects that were considered during the revision of Blossom were various features that promote interactivity with the data. For example, setting the granularity of data, but also delimiting different training sessions. Ultimately, the central core aspects were the smoothing of information, as sometimes huge amounts of data from many thousands of data points are generated in each training session and need to be visualized. In this context, performance issues also had to be carefully examined.

The aim of this contemplementary project was to write a module for the configuration and processing software used at Fraunhofer that implements the training configuration. The reason for this small project was to reduce the input in the terminal when you want to start a training or to move the input to the graphic.

As a result, the arguments in the terminal for the file paths to the architecture file and the configuration file can be saved, which keeps the command short and clear. Within the architecture file, the architecture, library etc. are specified in more detail, whereas the configuration file focuses more on hyperparameters such as learning rate and loss function. Now almost everything concerning the settings for the neural network can be realized graphically, namely in MeVisLab. MeVisLab is a graphical program where the training data for the neural network is usually preprocessed by editing and grouping the data using modules. These modules can be combined into a network to create an entire pipeline through which the training data automatically passes, and this is exactly where the module written in this small project can be found.

Although a machine learning model learns independently, there is a complex interaction between different software programs to create an environment that enables this training. These programs can output status messages as well as errors. Logs are therefore a valuable tool for the developer to monitor and improve the model.

DeepAnatomy uses a dashboard with which training can be started and monitored. In particular, this dashboard is intended to graphically display trainings (see "Blossom") and collect all relevant meta-information about the training. The aim of the Dashboard-Logger project was to extend the Dashboard web app independently in order to make logs from the training and MeVislab easily accessible and display them directly.

MeVislab is a program developed by Fraunhofer, which is used for data preparation and configuration of deep learning trainings. Both programs run in separate Docker containers in the container orchestration software "Nomad", so it is a distributed system. While the UI in Nomad itself only offers cumbersome ways to view all the logs associated with the training, we have simplified this in a central location and implemented useful additional functions. Due to the distributed system, the logs must first be collected in a central location for display: The basis for persisting the log data here is OpenSearch, into which the logs are pushed in real time using the log shipper pattern via fluentd. We can now request and display this log data via an Api interface.

Various considerations on topics such as UI design, UX and the general flow of information were incorporated and interesting mechanisms such as polling, infinite scroll, etc. were worked on.

The dashboard is an internal MEVIS project that can start trainings with given files. Access to GitLab repositories is to be implemented here so that the files can be taken directly from one or more repositories. To do this, repository access tokens must be set up and the possibility of integrating these via the front and back end must be set up using so-called vault keys.

Version 3.0 of Keras was released last year and offers new possibilities for writing deeplearning networks in Python. In particular, the framework now provides the flexibility to choose between tensorflow, Jax and Pytorch as a backend. Our task was to migrate the previously used Keras 2 to the new version. To do this, some of the existing Python modules had to be modified in accordance with the Keras 3 migration guide. Docker is used to ensure that all Fraunhofer employees work on the same versions of all frameworks. This allows a so-called image to be built, which authorized users can then download. This is built using a Dockerfile that specifies which versions are to be used. We built such an image and then carried out existing tests.

We then had to find solutions for the errors that occurred. However, some of the errors we encountered could not be fixed due to version incompatibilities between Keras3 and tensorflow_probabilities. Gradually, fixes were found for the remaining errors and even the version incompatibility was resolved by a new realease of Tensorflow. Further integration tests were then carried out, in which training sessions were started on the cluster via Nomad. As these did not run for various reasons, we also had to work on solutions to get the tests to run.