PatchSorter: A Deep Learning-Assisted Platform for Efficient Pathology Image Annotation

Ever feel like you’re drowning in a sea of microscopic images, desperately trying to identify and label every single cell? Yeah, we feel you. Pathology image annotation, while crucial for research and diagnostics, can feel like an endless game of “Where’s Waldo,” only with way more at stake than finding a guy in a striped shirt.

But fear not, fellow microscope jockeys! A new sheriff’s in town, and its name is PatchSorter (PS). This nifty software application is here to revolutionize the way we annotate regions of interest (ROIs) in digital pathology images. Think of it as your trusty sidekick, wielding the power of deep learning (DL) to make bulk labeling not just a dream but a reality. Say goodbye to tedious manual annotation and hello to a more efficient, streamlined workflow.

Unveiling the PatchSorter Workflow: From Chaos to Clarity

What truly sets PatchSorter apart is its user-friendly approach. No need to be a coding whiz or a DL guru to navigate this platform. It’s like having a GPS for your pathology images, guiding you through each step with ease. Let’s break down the workflow, shall we?

Project Creation: Your Digital Workspace Awaits

Imagine having a dedicated workspace for each of your annotation projects. With PatchSorter, that’s exactly what you get. Create individual projects, each acting as a container for your images, annotations, and model weights. It’s like having color-coded folders for your pathology adventures, keeping everything organized and chaos at bay.

Data Import: Bring on the Images!

Now it’s time to unleash your image data upon PatchSorter. Import those precious whole-slide images (WSIs) containing the ROIs you’re after. But wait, there’s more! Don’t forget to bring along the corresponding segmentation masks, those handy guides that highlight the exact location of your objects of interest. Think of them as the treasure maps leading you to those cellular goldmines.

Patch Extraction: Zeroing In on the Good Stuff

With the images and masks in place, PatchSorter gets to work, extracting patches of user-defined sizes centered around those highlighted objects. These patches, my friends, are the building blocks for the magic that’s about to unfold. They’ll be used for training the DL model and for your annotation endeavors.

Self-Supervised Feature Learning: Teaching the Machine to See

Here’s where things get really interesting. PatchSorter utilizes self-supervised learning, employing the mighty SimCLR with a ResNetbackbone, to learn a feature space representation of those extracted patches. In simpler terms, it’s like teaching the software to recognize patterns and similarities between different objects, all on its own. No need for manual labeling just yet, let the machine do the heavy lifting!

Dimensionality Reduction and Visualization: A Map of Cellular Similarities

Remember those feature vectors generated by the trained model? Well, PatchSorter takes those complex data points and projects them onto a two-dimensional embedding space using UMAP (Uniform Manifold Approximation and Projection). It’s like creating a map of your cellular landscape, where similar-looking objects cluster together. Think of it as a visual representation of cellular camaraderie.

Interactive Bulk Labeling: Unleash Your Inner Artist (and Annotator)

Get ready for some serious annotation power! With the embedding space laid out before you, it’s time to channel your inner artist and start labeling those objects. But here’s the cool part: you can label groups of similar objects all at once by simply drawing lasso selections around them. It’s like having a magic wand that labels multiple cells with a flick of your wrist! Say goodbye to the tedium of individually labeling each object and hello to a world of efficient bulk labeling.

Semi-Supervised Model Refinement: Learning from Your Expertise

As you diligently label those clusters, PatchSorter isn’t just sitting idly by. It’s constantly learning from your expertise, like a diligent student taking notes. The DL model gets refined using both the initial contrastive loss and a supervised cross-entropy loss based on your fresh annotations. It’s like a feedback loop of awesomeness, with each label you create making the feature space even more accurate and refined. Talk about a symbiotic relationship between human and machine!

Content-Based Image Retrieval: Find Your Perfect Match

Need to label a specific type of object but feeling lost in a sea of cells? Fear not, PatchSorter’s got your back! With its content-based image retrieval feature, you can upload a reference patch, and the software will tirelessly scour the dataset to retrieve visually similar objects. It’s like having a personal shopper for your pathology needs, bringing you all the best matches for efficient and targeted labeling.

Output and Integration: Seamlessly Connecting the Dots

So you’ve conquered the annotation battlefield, armed with PatchSorter’s powerful tools. Now what? Well, it’s time to reap the rewards of your labor! PatchSorter offers a variety of output options, from labeled mask images to direct access to its internal database for all you data-savvy folks. It’s all about seamless integration with downstream analysis pipelines, making your life easier every step of the way.

Putting PatchSorter to the Test: A Symphony of Efficiency and Accuracy

Okay, so PatchSorter sounds pretty amazing on paper, but how does it actually perform in the real world? Glad you asked! Its efficiency was put through the wringer, compared against a baseline approach using the trusty Quick Reviewer (QR), an open-source object labeling tool. Four distinct use cases were thrown its way, each more challenging than the last. Let’s dive into the nitty-gritty, shall we?

Use Case 1: Nuclei Labeling in Triple-Negative Breast Cancer

First up, a real crowd-pleaser: classifying lymphocyte and non-lymphocyte nuclei in breast cancer tissue. Talk about a cellular mosh pit! This use case was all about testing PatchSorter’s ability to handle high object density scenarios. Could it keep up with the cellular chaos?

Use Case 2: Detection of Tumor Budding in Pulmonary Squamous Cell Carcinoma

Next up, a game of hide-and-seek, but with much higher stakes. This use case focused on identifying those sneaky little clusters of cancer cells known as tumor buds. It was all about testing PatchSorter’s ability to detect subtle morphological features, like a cellular detective on the hunt for clues.

Use Case 3: Renal Tubular Classification

Hold on to your hats, folks, because things are about to get tubular! This use case involved classifying different types of renal tubules, showcasing PatchSorter’s ability to navigate complex tissue structures and differentiate between diverse object classes. It’s like sorting through a cellular jungle, but with much more precision.

Use Case 4: Renal Glomerular Classification

Last but not least, a test of PatchSorter’s ability to handle complex object morphology and challenging classification tasks. This use case focused on classifying glomeruli, those tiny filtration units in your kidneys, with varying degrees of scarring. It’s like grading cellular damage on a microscopic scale.

Efficiency Metrics and Results: A Resounding Victory for PatchSorter

Now for the moment of truth: the results! The primary efficiency metric used was “labels per second” (LPS), because time is precious, my friends. And guess what? PatchSorter blew the competition out of the water! It consistently outperformed the baseline QR approach across all four use cases, demonstrating substantial improvements in labeling speed. We’re talking about a significant boost in efficiency, folks.

But wait, there’s more! This efficiency gain didn’t come at the cost of accuracy. Nope, PatchSorter maintained high concordance scores between its labels and those generated by QR, proving that it’s not just fast but also incredibly accurate. It’s like having your cake and eating it too, but with fewer calories and more scientific breakthroughs.

PatchSorter: The Future of Pathology Image Annotation is Here

In a world where time is of the essence and accuracy is paramount, PatchSorter emerges as a true game-changer. It’s more than just a software application; it’s a powerful ally in the quest for scientific advancement. So, if you’re ready to ditch the tedium of manual annotation and embrace a future of efficiency, accuracy, and user-friendliness, then hop aboard the PatchSorter train. Trust us, your sanity (and your research) will thank you.