Paper Citation Record · LEDGER

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks

As of 17 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2504.21544.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.21544 v1

Coverage vector

measured 37 of 37 reference resolution

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

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A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

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Outbound references

Observation 71753e09-1304-4ccf-9c9c-f02c02405512 · outbound

This paper cites Giepmans, and George Azzopardi.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Giepmans, and George Azzopardi

Reference 1

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Observation db278c86-1b7c-46f5-b789-3364931cd248 · outbound

This paper cites The effects of aging on neuropil structure in mouse somatosensory cor- tex—a 3d electron microscopy analysis of layer 1.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks The effects of aging on neuropil structure in mouse somatosensory cor- tex—a 3d electron microscopy analysis of layer 1

Reference 2

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Observation d310eae1-9938-48fe-a162-dabce80cb897 · outbound

This paper cites Denseunet: densely connected unet for electron microscopy image seg- mentation.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Denseunet: densely connected unet for electron microscopy image seg- mentation

Reference 3

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Observation 80b0c148-10b5-4ec5-94a4-b6f8d70a54fd · outbound

This paper cites Sam-adapter: Adapting segment anything in underperformed scenes.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Sam-adapter: Adapting segment anything in underperformed scenes

Reference 4

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Observation 6d60d024-f5c4-48ec-9393-bd941af9102c · outbound

This paper cites UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images

Reference 5

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Observation c1528a96-0e35-4b04-ac6f-82f6797a204c · outbound

This paper cites Unleashing the potential of sam for medical adaptation via hierarchi- cal decoding.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Unleashing the potential of sam for medical adaptation via hierarchi- cal decoding

Reference 6

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Observation 7b080aa4-71ec-434f-9300-cdf329bacbb3 · outbound

This paper cites Residual de- convolutional networks for brain electron microscopy im- age segmentation.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Residual de- convolutional networks for brain electron microscopy im- age segmentation

Reference 7

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Observation c1eb7f65-da1e-4aed-b9ea-85b4207878d8 · outbound

This paper cites Jones, Yanling Liu, Dorsa Ziaei, Stephan Huschauer, Igna- cio Arganda-Carreras, Hanspeter Pfister, and Donglai Wei.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Jones, Yanling Liu, Dorsa Ziaei, Stephan Huschauer, Igna- cio Arganda-Carreras, Hanspeter Pfister, and Donglai Wei

Reference 8

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Observation 97a65723-4d3c-4856-b975-643c5e38d67b · outbound

This paper cites Parameter-efficient model adaptation for vision transformers.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Parameter-efficient model adaptation for vision transformers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:05:59.064078Z

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Observation 11fb8581-853a-4def-9889-f316a6e17228 · outbound

This paper cites Whole-cell organelle segmentation in volume electron microscopy.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Whole-cell organelle segmentation in volume electron microscopy

Reference 10

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Observation 47c76355-75ce-46cb-903f-d4be734de775 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks LoRA: Low-rank adaptation of large language models

Reference 11

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Observation ef894087-fbd2-402d-8245-dae341dc8c15 · outbound

This paper cites Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024

Reference 12

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Observation a5084260-af06-4643-b0cd-267f849cdc0d · outbound

This paper cites High-precision auto- mated reconstruction of neurons with flood-filling networks.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks High-precision auto- mated reconstruction of neurons with flood-filling networks

Reference 13

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Observation 9a91dae0-856d-4e44-adeb-059b8fc41440 · outbound

This paper cites Segment anything in high quality.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Segment anything in high quality

Reference 14

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Observation f942774c-a66f-4fe0-b00e-5eee5d710a95 · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick

Reference 15

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Observation a0a742e2-5d3e-4b70-837a-c390b5e9ae95 · outbound

This paper cites Asam: Boosting segment anything model with adversarial tuning.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Asam: Boosting segment anything model with adversarial tuning

Reference 16

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Observation 03da6563-e51e-43fc-8ef9-eb86f43b0071 · outbound

This paper cites Transformer-based visual segmenta- tion: A survey.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Transformer-based visual segmenta- tion: A survey

Reference 17

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Observation a241e4e1-4c7d-4aa5-9d3c-670f557afdf2 · outbound

This paper cites Learning cross-representation affinity consistency for sparsely supervised biomedical instance segmentation.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Learning cross-representation affinity consistency for sparsely supervised biomedical instance segmentation

Reference 18

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Observation cd840c9c-951e-4846-9bfc-f0d3a3042794 · outbound

This paper cites Supervoxel-based segmentation of mitochondria in em image stacks with learned shape fea- tures.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Supervoxel-based segmentation of mitochondria in em image stacks with learned shape fea- tures

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:05:58.933291Z

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Observation a128f4ee-dbac-4f5c-aa74-736e8865fc75 · outbound

This paper cites Electron microscopy images as set of fragments for mitochondrial segmentation.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Electron microscopy images as set of fragments for mitochondrial segmentation

Reference 20

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Observation 23ed2a47-d640-4474-ae69-664a83d8cb91 · outbound

This paper cites Rankmatch: Exploring the better consistency regularization for semi-supervised semantic segmentation.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Rankmatch: Exploring the better consistency regularization for semi-supervised semantic segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:05:58.905350Z

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Observation 14151527-2b6f-4a77-822a-70c6a0ed7db7 · outbound

This paper cites Dualrel: Semi-supervised mitochondria seg- mentation from a prototype perspective.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Dualrel: Semi-supervised mitochondria seg- mentation from a prototype perspective

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:05:58.891313Z

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Observation e8619e6f-d7ab-4a3c-b8ff-a8b7358be156 · outbound

This paper cites Automatic segmentation and reconstruction of intracellular compartments in volumetric electron microscopy data.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Automatic segmentation and reconstruction of intracellular compartments in volumetric electron microscopy data

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:05:58.877138Z

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Observation 0271f873-9ec4-44b7-ae55-f42b914fa835 · outbound

This paper cites Adaptive tem- plate transformer for mitochondria segmentation in electron microscopy images.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Adaptive tem- plate transformer for mitochondria segmentation in electron microscopy images

Reference 24

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Observation 69902ae3-746e-4497-b990-ab483b45e79e · outbound

This paper cites V olume electron microscopy.Nature Reviews Methods Primers, 2(1):51, 2022.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks V olume electron microscopy.Nature Reviews Methods Primers, 2(1):51, 2022

Reference 25

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Observation 063e17a8-623d-459b-82cc-fe5ca5f09ebc · outbound

This paper cites Parameter efficient fine-tuning via cross block orchestration for segment anything model.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Parameter efficient fine-tuning via cross block orchestration for segment anything model

Reference 26

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Observation a05d3afb-189b-46c2-b6f1-9ff1072b27f9 · outbound

This paper cites Fusionnet: A deep fully residual convo- lutional neural network for image segmentation in connec- tomics.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Fusionnet: A deep fully residual convo- lutional neural network for image segmentation in connec- tomics

Reference 27

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Observation 856a8568-5e86-4950-9dfe-18f7e5f72114 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks SAM 2: Segment Anything in Images and Videos

Reference 28

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Observation efd664c2-f5b0-4076-8061-66d2be1926c2 · outbound

This paper cites Berger, Art Pope, Yuelong Wu, Tim Blakely, Richard L.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Berger, Art Pope, Yuelong Wu, Tim Blakely, Richard L

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:05:58.808430Z

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Observation 09f69afc-eadb-4c31-8f02-51b829d81c53 · outbound

This paper cites Daw: Exploring the better weighting function for semi-supervised semantic segmentation.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Daw: Exploring the better weighting function for semi-supervised semantic segmentation

Reference 30

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Observation f06013bb-b5a7-4d8d-aa92-d7349516c55b · outbound

This paper cites Automated synapse-level reconstruction of neural circuits in the larval zebrafish brain.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Automated synapse-level reconstruction of neural circuits in the larval zebrafish brain

Reference 31

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Observation a5239325-3548-4bf5-888c-5979bc8b2793 · outbound

This paper cites Lichtman, and Hanspeter Pfister.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Lichtman, and Hanspeter Pfister

Reference 32

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Observation ec15e102-05a4-49b7-af0a-08eedd8cdd7f · outbound

This paper cites Efficientsam: Leveraged masked image pre- training for efficient segment anything.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Efficientsam: Leveraged masked image pre- training for efficient segment anything

Reference 33

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Observation 3509c5fb-ab9c-4233-b534-dc1815f0f8de · outbound

This paper cites Efficient low-rank backprop- agation for vision transformer adaptation.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Efficient low-rank backprop- agation for vision transformer adaptation

Reference 34

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Observation d6482cb6-074e-458f-bfce-89a178718b64 · outbound

This paper cites Hive-net: Centerline-aware hierarchical view- ensemble convolutional network for mitochondria segmen- tation in em images.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Hive-net: Centerline-aware hierarchical view- ensemble convolutional network for mitochondria segmen- tation in em images

Reference 35

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Observation 052129ce-aff6-4b48-9a0b-69d2ec03757a · outbound

This paper cites Customized Segment Anything Model for Medical Image Segmentation.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Customized Segment Anything Model for Medical Image Segmentation

Reference 36

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Observation b4d88388-249f-4c1f-9bce-01b1c1306154 · outbound

This paper cites Segment any- thing model for medical image segmentation: Current ap- plications and future directions.

SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks Segment any- thing model for medical image segmentation: Current ap- plications and future directions

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:05:58.709476Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

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