  Vision-Language Pretraining (VLP) has demonstrated remarkable capabilities in
learning visual representations from textual descriptions of images without
annotations. Yet, effective VLP demands large-scale image-text pairs, a
resource that suffers scarcity in the medical domain. Moreover, conventional
VLP is limited to 2D images while medical images encompass diverse modalities,
often in 3D, making the learning process more challenging. To address these
challenges, we present Generative Text-Guided 3D Vision-Language Pretraining
for Unified Medical Image Segmentation (GTGM), a framework that extends of VLP
to 3D medical images without relying on paired textual descriptions.
Specifically, GTGM utilizes large language models (LLM) to generate
medical-style text from 3D medical images. This synthetic text is then used to
supervise 3D visual representation learning. Furthermore, a negative-free
contrastive learning objective strategy is introduced to cultivate consistent
visual representations between augmented 3D medical image patches, which
effectively mitigates the biases associated with strict positive-negative
sample pairings. We evaluate GTGM on three imaging modalities - Computed
Tomography (CT), Magnetic Resonance Imaging (MRI), and electron microscopy (EM)
over 13 datasets. GTGM's superior performance across various medical image
segmentation tasks underscores its effectiveness and versatility, by enabling
VLP extension into 3D medical imagery while bypassing the need for paired text.
