Troubleshooting Stardist Segmentation Failures

Dec 5, 2025 by Alex Johnson 47 views

Encountering errors, especially when a process is nearly complete, can be incredibly frustrating. If you're facing a situation where Stardist fails at around 95%, particularly when working with CosMx data via Snakemake, you're not alone. This kind of issue often points to a complex interplay of factors, from data peculiarities to resource limitations or software configurations. Let's dive deep into potential causes and solutions to get your segmentation pipeline back on track. This article aims to provide a comprehensive guide to diagnose and resolve these stubborn 95% failures, ensuring smoother and more reliable results in your biological image analysis.

Understanding the 95% Failure Phenomenon

When Stardist fails at around 95%, it often signifies that the bulk of the heavy lifting – the initial training, prediction, and most of the segmentation process – has been completed successfully. The failure at this late stage typically implies an issue with a specific, often resource-intensive, finalization step, or a problem that only manifests when processing the very last portion of your data. This could be related to memory management, disk I/O, inter-process communication in a parallelized workflow like Snakemake, or even subtle data inconsistencies that only trip up the algorithm in its final stages. It's crucial to approach this with a systematic debugging mindset, examining logs closely for any clues that might point to the specific operation that failed. Often, the error message, though cryptic, is the key to unlocking the solution. In the context of CosMx data, which can be quite large and complex, these late-stage failures are not uncommon and require careful consideration of the entire processing pipeline, from data loading to output generation.

Deep Dive into Potential Causes

Let's break down the common culprits behind Stardist failures at 95%.

Memory Exhaustion: Even if your system has ample RAM, specific operations within Stardist, especially when handling large datasets like those from CosMx, might require a sudden spike in memory. This can happen during the final assembly of results, saving large output files, or when processing the last few image tiles if the data is tiled. The fact that it fails late suggests that cumulative memory usage might be the issue, or a specific data structure used for the final output is unexpectedly large. Snakemake, in its parallel execution, can also exacerbate this if multiple jobs concurrently demand significant memory.
Disk Space or I/O Issues: The final stages of segmentation often involve writing substantial amounts of data to disk. This could be segmentation masks, probability maps, or other processed outputs. If your disk is nearly full, or if there are issues with the write speed or stability of your storage, the process can halt unexpectedly. Large CosMx datasets can generate output files that are gigabytes in size, so ensuring sufficient free space and a robust I/O system is paramount.
Corrupted or Inconsistent Data: While less common, it's possible that a small segment of your CosMx data is malformed, contains unexpected values (like NaNs or Infs), or has a different format than the rest. Stardist might process most of the data without issue, but encounter a problem when it tries to process this specific problematic section near the end. A thorough data integrity check, especially for the latter parts of your dataset, could be beneficial.
Software Dependencies and Environment: Although your system appears to have a comprehensive set of dependencies (as listed), subtle version conflicts or incompatibilities can sometimes lead to errors that only surface under specific conditions, like at the end of a long computation. Ensure that all dependencies, particularly stardist, cellpose (if used as a backend or for comparison), napari, spatialdata, and their core scientific libraries like numpy, scipy, tensorflow, and torch, are compatible and up-to-date. A clean, isolated environment (e.g., using Conda or virtualenv) is always recommended.
Snakemake Configuration and Resource Management: If you're running Stardist through Snakemake, the way resources (CPU, memory) are allocated can play a role. A job might be running out of memory allocated by Snakemake, even if the system has more available globally. Check your config.yaml or rule definitions for resources like mem_mb or tmp_dir settings. Sometimes, a temporary directory used for intermediate files might be filling up or running out of space.
Stardist Model or Parameters: While unlikely to cause a failure only at 95%, it's worth considering if any specific parameters used in your Stardist model or prediction step could be sensitive to certain data characteristics that appear late in the dataset. For instance, a thresholding parameter might be misbehaving on a final, slightly different, data region.
Underlying Libraries (TensorFlow/PyTorch): Stardist relies heavily on deep learning frameworks like TensorFlow or PyTorch. Issues within these frameworks, especially related to GPU usage, CUDA drivers, or specific operations that are heavily optimized, can sometimes manifest as obscure errors during long computations. Ensure your TensorFlow/PyTorch installation is stable and compatible with your hardware and drivers.

Debugging Strategies for Stardist Failures

To effectively troubleshoot Stardist failing at 95%, employ these strategies:

Examine Logs Meticulously: The provided log snippets are crucial. Look for the exact error message, traceback, and any warnings preceding the failure. The snakemake.log and any specific log files generated by the Stardist rule are your primary source of information. Often, the last few lines before the crash contain the most valuable clues.
Simplify and Isolate: If possible, try running Stardist on a smaller subset of your data, focusing on the problematic region if you can identify it. This helps determine if the issue is data-volume related or specific to a particular data chunk. You could also try running Stardist directly (outside of Snakemake) on a small problematic sample to see if the error persists.
Monitor System Resources: While the job is running, actively monitor your system's RAM, CPU, and disk usage. Tools like htop, iotop, or your system's graphical monitoring tools can reveal if memory is being exhausted or if disk I/O is bottlenecking. If using a cluster, check the job logs for resource usage.
Increase Memory Allocation (Snakemake): If memory seems to be the culprit, try increasing the memory allocated to the Stardist rule in your Snakemake configuration. This might involve adjusting resources: mem_mb: in your rule definition.
Check Disk Space: Ensure you have significantly more free disk space than the expected output size. Temporary directories used by Snakemake or intermediate files can also consume a lot of space; check the tempdir setting if applicable.
Test Different Stardist Versions or Models: If you suspect a software bug, try running with a slightly older or newer version of Stardist, or a different pre-trained model if applicable. This can help rule out version-specific issues.
Verify Data Integrity: Implement checks to ensure your CosMx data is clean. This might involve custom scripts to check for NaNs, Infs, or unexpected data types in the problematic data segments.
Review Dependencies: While your listed dependencies are extensive, ensure there are no known conflicts with your specific Python version (3.10.16) or the versions of key libraries like TensorFlow/PyTorch. Consider creating a fresh Conda environment with minimal, known-good versions of Stardist and its core dependencies.

By systematically applying these debugging steps, you should be able to pinpoint the exact cause of the Stardist failure at 95% and implement a suitable fix. Remember that detailed log analysis and controlled experimentation are your most powerful tools in resolving such complex issues.

Addressing Specific Error Logs

Let's assume, hypothetically, that the error logs you shared (though not visible to me in this format) contain a message like Killed or MemoryError. This strongly reinforces the hypothesis of memory exhaustion causing Stardist to fail. When a process receives a Killed signal, it often means the operating system's Out-Of-Memory (OOM) killer has intervened. This happens when a process consumes more memory than is available and allocated, and the OS terminates it to prevent system instability. In a Snakemake workflow, this could mean the memory requested for the Stardist job was insufficient, or that cumulative memory usage across multiple Snakemake jobs exceeded system capacity.

If the error message is more specific, such as a tensorflow.python.framework.errors_impl.ResourceExhaustedError, it directly indicates that the TensorFlow backend used by Stardist ran out of memory, likely GPU memory if a GPU is being used. This is common during large tensor operations, which can occur during the final aggregation of predictions or when saving complex model outputs. The fix here would involve either:

Reducing Batch Size: If Stardist allows (though it's often determined by the model itself), reducing batch sizes can lower memory requirements. However, for inference, this is less of a controllable parameter.
Increasing GPU Memory: If running on a machine with a GPU, ensure the job has access to sufficient GPU RAM. This might involve running on a machine with a more powerful GPU or optimizing other processes that might be consuming GPU memory.
CPU Fallback: If you are using a GPU and encountering memory issues, try running the segmentation on CPU only, which usually has more available memory, albeit at a much slower speed. You might need to adjust your TensorFlow configuration for this.
Data Parallelism Tuning (Snakemake): If your Snakemake workflow is configured to run multiple Stardist instances in parallel, you might need to limit the number of concurrent jobs or adjust the resources allocated to each to prevent overall system memory exhaustion. This is where checking your Snakemake config.yaml and rule definitions for threads and resources settings becomes critical.

Another potential error message could relate to file operations, like OSError: [Errno 28] No space left on device. This clearly points to disk space limitations. Even if you have ample space at the start, intermediate files or the final large output files might exceed available storage. CosMx datasets can be very dense, leading to large segmentation masks or probability maps. Ensure that both the primary storage and any temporary directories (tmpdir in Snakemake) have sufficient free space. Regularly cleaning up temporary files generated by Snakemake can also help.

If the error is more obscure, perhaps related to internal data structures or object serialization, it might suggest a subtle bug or an edge case in how Stardist handles specific data patterns, especially at the end of a large file or dataset. In such cases, consulting the Stardist GitHub issues page or its community forums for similar reported problems can be very insightful. Providing detailed logs, system information, and the specific data characteristics you are using is crucial for community support.

Conclusion: Moving Forward with Stardist

Resolving Stardist segmentation failures at 95% requires patience and a systematic approach. By carefully analyzing your logs, monitoring system resources, and understanding the potential bottlenecks related to memory, disk I/O, data integrity, and software environment, you can effectively diagnose and fix the underlying issues. Remember to leverage the power of your workflow manager, like Snakemake, to manage resources efficiently and isolate problems. Don't hesitate to consult community forums and GitHub repositories for Stardist and its dependencies, as shared experiences can often provide the quickest path to a solution.

For further insights into advanced image analysis techniques and common pitfalls, the CellProfiler documentation offers a wealth of information on image processing best practices that can be indirectly helpful in understanding segmentation challenges. Additionally, exploring the SpatialData documentation can provide context on handling large, complex spatial omics datasets, which is directly relevant to your CosMx data.