  In digital pathology, the traditional method for deep learning-based image
segmentation typically involves a two-stage process: initially segmenting
high-resolution whole slide images (WSI) into smaller patches (e.g., 256x256,
512x512, 1024x1024) and subsequently reconstructing them to their original
scale. This method often struggles to capture the complex details and vast
scope of WSIs. In this paper, we propose the holistic histopathology
(HoloHisto) segmentation method to achieve end-to-end segmentation on gigapixel
WSIs, whose maximum resolution is above 80,000$\times$70,000 pixels. HoloHisto
fundamentally shifts the paradigm of WSI segmentation to an end-to-end learning
fashion with 1) a large (4K) resolution base patch for elevated visual
information inclusion and efficient processing, and 2) a novel sequential
tokenization mechanism to properly model the contextual relationships and
efficiently model the rich information from the 4K input. To our best
knowledge, HoloHisto presents the first holistic approach for gigapixel
resolution WSI segmentation, supporting direct I/O of complete WSI and their
corresponding gigapixel masks. Under the HoloHisto platform, we unveil a random
4K sampler that transcends ultra-high resolution, delivering 31 and 10 times
more pixels than standard 2D and 3D patches, respectively, for advancing
computational capabilities. To facilitate efficient 4K resolution dense
prediction, we leverage sequential tokenization, utilizing a pre-trained image
tokenizer to group image features into a discrete token grid. To assess the
performance, our team curated a new kidney pathology image segmentation (KPIs)
dataset with WSI-level glomeruli segmentation from whole mouse kidneys. From
the results, HoloHisto-4K delivers remarkable performance gains over previous
state-of-the-art models.
